research definition of work

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• v.34; 2021 Dec

What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire

Anne pignault.

1 Université de Lorraine, Psychology & Neuroscience Laboratory (2LPN, EA7489), 23 boulevard Albert 1er, 54000 Nancy, France

Claude Houssemand

2 University of Luxembourg, Department of Education and Social Work, Institute for Lifelong Learning & Guidance (LLLG), 2 Avenue de l’Université, L-4365 Esch-sur-Alzette, Luxembourg

Associated Data

The datasets generated and/or analyzed during the current study are available from the corresponding author.

Considering the recent and current evolution of work and the work context, the meaning of work is becoming an increasingly relevant topic in research in the social sciences and humanities, particularly in psychology. In order to understand and measure what contributes to the meaning of work, Morin constructed a 30-item questionnaire that has become predominant and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, it has been validated only in part.

Meaning of work questionnaire was conducted in French with 366 people (51.3% of women; age: ( M = 39.11, SD = 11.25); 99.2% of whom were employed with the remainder retired). Three sets of statistical analyses were run on the data. Exploratory and confirmatory factor analysis were conducted on independent samples.

The questionnaire described a five-factor structure. These dimensions (Success and Recognition at work and of work, α = .90; Usefulness, α = .88; Respect for work, α = .88; Value from and through work, α = .83; Remuneration, α = .85) are all attached to a general second-order latent meaning of work factor (α = .96).

Conclusions

Validation of the scale, and implications for health in the workplace and career counseling practices, are discussed.

Introduction

Since the end of the 1980s, many studies have been conducted to explore the meaning of work, particularly in psychology (Rosso, Dekas, & Wrzesniewski, 2010 ). A review of the bibliographical data in PsychInfo shows that between 1974 and 2006, 183 studies addressed this topic (Morin, 2006 ). This scholarly interest was primarily triggered by Sverko and Vizek-Vidovic’s ( 1995 ) article, which identified the approaches and models that have been used and their main results.

Whereas early studies on the meaning of work introduced the concept and its theoretical underpinnings (e.g., Harpaz, 1986 ; Harpaz & Fu, 2002 ; Morin, 2003 ; MOW International Research team, 1987 ), later research tried to connect this aspect of work with other psychological dimensions or individual perceptions of the work context (e.g., Harpaz & Meshoulam, 2010 ; Morin, 2008 ; Morin, Archambault, & Giroux, 2001 ; Rosso et al., 2010 ; Wrzesniewski, Dutton, & Debebe, 2003 ). Nevertheless, scholars, particularly those in organizational and occupational psychology, soon found it difficult to precisely identify the meaning of work because it changes in accordance with the conceptualizations of different researchers, the theoretical models used to describe it, and the tools that are available to measure it for individuals and for groups.

This article first seeks to clarify the concept of the meaning of work (definitions and models) before bringing up certain problems involved in its measurement and the diversity in how the concept has been used. Then the paper focuses on a particular meaning of work measurement tool developed in Canada, which is now widely used in French-speaking countries. At the beginning of the twenty-first century, Morin et al. ( 2001 ) developed a 30-item questionnaire to better determine the dimensions that give meaning to a person’s work. The statistical analyses needed to determine the reliability and validity of Morin et al.’s meaning of work questionnaire have never been completed. Indeed, some changes were made to the initial scale, and the analyses only based on homogenous samples of workers in different professional sectors. Thus and even though the meaning of work scale is used quite frequently, both researchers and practitioners have been unsure about whether or not to trust its results. The main objective of the present study was thus to provide a psychometric validation of Morin et al.’s meaning of work scale and to uncover its latent psychological structure.

Meaning of work: from definition to measurement

Meaning of work: what is it.

As many scholars have found, the concept of the meaning of work is not easy to define (e.g., Rosso et al., 2010 ). In terms of theory, it has been defined differently in different academic fields. In psychology, it refers to an individual’s interpretations of his/her actual experiences and interactions at work (Ros, Schwartz, & Surkiss, 1999 ). From a sociological point of view, it involves assessing meaning in reference to a system of values (Rosso et al., 2010 ). In this case, its definition depends on cultural or social differences, which make explaining this concept even more complex (e.g., Morse & Weiss, 1955 ; MOW International Research team, 1987 ; Steers & Porter, 1979 ; Sverko & Vizek-Vidovic, 1995 ).

At a conceptual level, the meaning of work has been defined in three different ways (Morin, 2003 ). First, it can refer to the meaning of work attached to an individual’s representations of work and the values he/she attributes to that work (Morse & Weiss, 1955 ; MOW International Research team, 1987 ). Second, it can refer to a personal preference for work as defined by the intentions that guide personal action (Super & Sverko, 1995 ). Third, it can be understood as consistency between oneself and one’s work, similar to a balance in one’s personal relationship with work (Morin & Cherré, 2004 ).

With respect to terms, some differences exist because the meaning of work is considered an individual’s interpretation of what work means or of the role it plays in one’s life (Pratt & Ashforth, 2003 ). Yet this individual perception is also influenced by the environment and the social context (Wrzesniewski et al., 2003 ). The psychological literature on the meaning of work has primarily examined its positive aspects, even though work experiences can be negative or neutral. This partiality about the nature of the meaning of work in research has led to some confusion in the literature between this concept and that of meaningful , which refers to the extent to which work has personal significance (a quantity) and seems to depend on positive elements (Steger, Dik, & Duffy, 2012 ). A clearer demarcation should be made between these terms in order to specify the exact sense of the meaning of work: “This would reserve ‘meaning’ for instances in which authors are referring to what work signifies (the type of meaning), rather than the amount of significance attached to the work” (Rosso et al., 2010 , p. 95).

The original idea of the meaning of work refers to the central importance of work for people, beyond the simple behavioral activity through which it occurs. Drawing on various historical references, certain authors present work as an essential driver of human life; these scholars then seek to understand how work is fundamental (e.g., Morin, 2006 ; Sverko & Vizek-Vidovic, 1995 ). The concept of the meaning of work is connected to the centrality of work for the individual and consequently fulfills four different important functions: economic (to earn a living), social (to interact with others), prestige (social position), and psychological (identity and recognition). In this view, the centrality of work is based on an ensemble of personal and social values that differ between individuals as well as between cultures, economic climates, and occupations (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ).

Meaning of work: which theoretical model?

The first theoretical model for the meaning of work was based on research in the MOW project (MOW International Research team, 1987 ), considered the “most empirically rigorous research ever undertaken to understand, both within and between countries, the meanings people attach to their work roles” (Brief, 1991 , p. 176). This view suggests that the meaning of work is based on five principal theoretical dimensions: work centrality as a life role, societal norms regarding work, valued work outcomes, importance of work goals, and work-role identification. A series of studies on this theory was conducted in Israel (Harpaz, 1986 ; Harpaz & Fu, 2002 ; Harpaz & Meshoulam, 2010 ), complementing the work of the MOW project (MOW International Research team, 1987 ). Harpaz ( 1986 ) empirically identified six latent factors that represent the meaning of work: work centrality, entitlement norm, obligation norm, economic orientation, interpersonal relations, and expressive orientation.

Another theoretical model on the importance of work in a person’s life was created by Sverko in 1989 . This approach takes into account the interactions among certain work values (the importance of these values and the perception of possible achievements through work), which depend on a process of socialization. The ensemble is then moderated by an individual’s personal experiences with work. In the same vein, Rosso et al. ( 2010 ) tried to create an exhaustive model of the sources that influence the meaning of work. This model is built around two major dimensions: Self-Others (individual vs. other individuals, groups, collectives, organizations, and higher powers) and Agency-Communion (the drives to differentiate, separate, assert, expand, master, and create vs. the drives to contact, attach, connect, and unite). This theoretical framework describes four major pathways to the meaning of work: individuation (autonomy, competence, and self-esteem), contribution (perceived impact, significance, interconnection, and self-abnegation), self-connection (self-concordance, identity affirmation, and personal engagement), and unification (value systems, social identification, and connectedness).

Lastly, a more recent model (Lips-Wiersma & Wright, 2012 ) converges with the theory suggested by Rosso et al. ( 2010 ) but distinguishes two dimensions: Self-Others versus Being-Doing. This model describes four pathways to meaningful work: developing the inner self, unity with others, service to others, and expressing one’s full potential.

Without claiming to be exhaustive, this brief presentation of the theoretical models of the meaning of work underscores the difficulty in precisely defining this concept, the diversity of possible approaches to identifying its contours, and therefore implicitly addresses the various tools designed to measure it.

Measuring the meaning of work

Various methodologies have been used to better determine the concept of the meaning of work and to grasp what it involves in practice. The tools examined below have been chosen because of their different methodological approaches.

One of the first kinds of measurements was developed by the international MOW project (MOW International Research team, 1987 ). In this study, England and Harpaz ( 1990 ) and Ruiz-Quintanilla and England ( 1994 ) used 14 defining elements to assess agreement on the perception of work of 11 different sample groups questioned between 1989 and 1992. These elements, resulting from the definition of work given by the MOW project and studied by applying multivariate analyses and textual content analyses ( When do you consider an activity as working ? Choose four statements from the list below which best define when an activity is “ working,” MOW International Research team, 1987 ), can be grouped into four distinct heuristic categories (Table ​ (Table1 1 ).

Items used to define the concept of work

These items were taken from Ruiz-Quintanilla and England ( 1994 ). The letter in front of each item corresponds to the initial order of the items (MOW International Research team, 1987 )

Similarly, England ( 1991 ) studied changes in the meaning of work in the USA between 1982 and 1989. He used four different methodological approaches to the meaning of work: societal norms about work, importance of work goals, work centrality, and definition of work by the labor force. In the wake of these studies, others developed scales to measure the centrality of work in people’s lives, either for the general population (e.g., Warr, 2008 ) or for specific subpopulations such as unemployed people, on the basis of a rather similar conceptualization of the meaning of work (McKee-Ryan, Song, Wanberg, & Kinicki, 2005 ; Wanberg, 2012 ).

Finally, Wrzesniewski, McCauley, Rozin, and Schwartz ( 1997 ) developed a rather unusual method for evaluating people’s relationships with their work. Although not directly connected to research on the meaning of work, this study and the questionnaire they used ( University of Pennsylvania Work-Life Questionnaire ) addressed some of the same concepts. Above all, they employed the concepts in a very particular way that combined psychological scales, scenarios, and sociodemographic questions. Through these scenarios (Table ​ (Table2) 2 ) and the extent to which the respondents felt like the described characters, their relationship to work was described as either a Job, a Career, or a Calling.

Scenarios used to measure the relationship to work

These scenarios were taken from Wrzesniewski et al. ( 1997 , p. 24)

This presentation of certain tools for measuring the meaning of work reveals a variety of methodological approaches. Nevertheless, whereas certain methods have adopted a rather traditional psychological approach, others are often difficult to use for various reasons such as their psychometrics (e.g., the use of only one item to measure a concept; England, 1991 ; Wrzesniewski et al., 1997 ) or for practical reasons (e.g., the participants were asked questions that pertained not only to their individual assessment of work but also to various other parts of their lives; England, 1991 ; Warr, 2008 ). This diversity in the possible uses of the meaning of work makes it difficult to select a tool to measure it.

In French-speaking countries (Canada and Europe primarily), the previously mentioned scale created by Morin et al. ( 2001 ) has predominated and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, there has not been a complete validation of the scale (i.e., different forms of the same tool, only the use of exploratory factor analyses, and no similar structures found) that was the motivation for the current study.

The present study

The present article conceives of the meaning of work as representing a certain consistency between what an individual wants out of work and the individual’s perception, lived or imagined, of his/her work. It thus corresponds to the third definition of the meaning of work presented above—consistency between oneself and one's work (Morin & Cherré, 2004 ). This definition is strictly limited to the meaning given to work and the personal significance of this work from the activities that the work implies. Within this conceptual framework, some older studies adopted a slightly different cognitive conception, in which individuals constantly seek a balance between themselves and their environment, and any imbalance triggers a readjustment through which the person attempts to stabilize his/her cognitive state (e.g., Heider, 1946 ; Osgood & Tannenbaum, 1955 ). Here, the meaning of work must be considered a means for maintaining psychological harmony despite the destabilizing events that work might involve. In this view, meaning is viewed as an effect or a product of the activity (Brief & Nord, 1990 ) and not as a permanent or fixed state. It then becomes a result of person-environment fit and falls within the theory of work adjustment (Dawis, Lofquist, & Weiss, 1968 ).

Within this framework, a series of recurring and interdependent studies should be noted (e.g., Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ) because they have attempted to measure the coherence that a person finds in the relation between the person’s self and his/her work and thus implicitly the meaning of that work. Therefore, these studies make it possible to understand the meaning of work in greater detail, meaning that it could be used in practice through a self-evaluation questionnaire. The level of coherence is considered the degree of similarity between the characteristics of work that the person attributes meaning to and the characteristics that he/she perceives in his/her present work (Aronsson, Bejerot, & Häremstam, 1999 ; Morin & Cherré, 2004 ). Based on semi-structured interviews and on older research related to the quality of life at work (Hackman & Oldham, 1976 ; Ketchum & Trist, 1992 ), a model involving 14 characteristics was developed: the usefulness of work, the social contribution of work, rationalization of the tasks, workload, cooperation, salary, the use of skills, learning opportunities, autonomy, responsibilities, rectitude of social and organizational practices, the spirit of service, working conditions, and, finally, recognition and appreciation (Morin, 2006 ; Morin & Cherré, 1999 ). Then, based on this model, a 30-item questionnaire was developed to offer more precise descriptions of these dimensions. Table ​ Table3 3 presents the items, which were designed and administered to the participants in French.

Items from the meaning of work scale by Morin with their theoretical dimensions and exploratory factor analyses

P personal power at work, U usefulness of work, R success at work, A autonomy at work, S safety, E ethics, UT usefulness of work, VP personal value, EF personal efficacy, ET ethics of work, RT rationalization of work, IE personal influence

(*) = French version. 1 = Morin and Cherré ( 1999 ). 2 = Morin et al. ( 2001 ) and Morin ( 2003 ). 3 = Morin and Cherré ( 2004 )

Some studies for structurally validating this questionnaire have been conducted over the years (e.g., Morin, 2003 , 2006 , 2008 ; Morin & Cherré, 2004 ). However, their results were not very precise or comparable. For example, the number of latent factors in the meaning of work scale structure varied (e.g., six or eight factors: Morin, 2003 ; six factors: Morin, 2006 ; Morin & Cherré, 2004 ), the sample groups were not completely comparable (especially with respect to occupations), and finally, items were added or removed or their phrasing was changed (e.g., 30 and 33 items: Morin, 2003 ; 30 items: Morin, 2006 ; 26 items: Morin, 2008 ). Yet the most prominent methodological problem was that only exploratory analyses (most often a principal component analysis with varimax rotation) had been applied. This scale was entirely relevant from a theoretical point of view because it offered a more specific definition of the meaning of work than other scales and, mainly, because some subdimensions appeared to be linked with anxiety, depression, irritability, cognitive problems, psychological distress, and subjective well-being (Morin et al., 2001 ). It was also relevant from a practical point of view because it was short and did not take much time to complete. However, its use was questionable because it had never been validated psychometrically, and a consistent latent psychological structure had not been identified across studies.

As an example, two models representing the structure of the 30-item scale are presented in Table ​ Table3 3 (Morin et al., 2001 ; Morin, 2003 , for the first model; Morin & Cherré, 2004 , for the second one). This table presents the items, the meaning of work dimensions they are theoretically related to, and the solution from the principal component analysis in each study. These analyses revealed that the empirical and theoretical structures of this tool are not stable and that the latent structure suffers from the insufficient use of statistical methods. In particular, there was an important difference found between the two models in previous studies (Morin et al., 2001 ; Morin & Cherré, 2004 ). Only the “usefulness of work” dimension was found to be identical, comprised of the same items in both models. Other dimensions had a maximum of only three items in common. Therefore, it is very difficult to utilize this tool both in practice and diagnostically, and complementary studies must be conducted. Even though there are techniques for replicating explanatory analyses (e.g., Osborne, 2012 ), such techniques could not be used here because not all the necessary information was given (e.g., all factor loadings, communalities). This is why collecting new data appeared to be the only way to analyze the scale.

More recently, two studies (which applied a new 25-item meaningful work questionnaire ) were developed on the basis of Morin’s scale (Bendassolli & Borges-Andrade, 2013 ; Bendassolli, Borges-Andrade, Coelho Alves, & de Lucena Torres, 2015 ). Even though the concepts of the “meaning of work” and “meaningful work” are close, the two scales are formally and theoretically different and do not evaluate the same construct.

The purpose of the present study was thus to determine the structure of original Morin’s 30-item scale (Morin, 2003 ; Morin & Cherré, 2004 ) by using an exploratory approach as well as confirmatory statistical methods (structural equation modeling) and in so doing, to address the lacunae in previous research discussed above. The end goal was thus to identify the structure of the scale statistically so that it can be used empirically in both academic and professional fields. Indeed, as mentioned previously, this scale is of particular interest to researchers because its design is not limited to measuring a general meaning of work for each individual; it can also be used to evaluate discrepancies or a convergence between a person’s own personal meaning of work and a specific work context (e.g., tasks, relations with others, autonomy). Finally, and with respect to previous results, the scale could be a potential predictor of professional well-being and psychological distress at work (Morin et al., 2001 ).

Participants

The questionnaire was conducted with 366 people who were mainly resident in Paris and the surrounding regions in France. The gender distribution was almost equal; 51.3% of the respondents were women. The respondents’ ages ranged from 19 to 76 years ( M = 39.11, SD = 11.25). The large majority of people were employed (99.2%). Twenty percent worked in medical and paramedical fields, 26% in retail and sales, and 17% in human resources (the other respondents worked in education, law, communication, reception, banking, and transportation). Seventy percent had fewer than 10 years of seniority in their current job ( M = 8.64, SD = 9.65). Only three people were retired (0.8%).

Morin’s 30-item meaning of work questionnaire (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ) along with sociodemographic questions (i.e., sex, age, job activities, and seniority at work) were conducted in French through an online platform. Answers to the meaning of work questionnaire were given on a 5-point Likert scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ).

Participants were recruited through various professional online social networks. This method does not provide for a true random sample but, owing to it resulting in a potentially larger range of respondents, it enlarges the heterogeneousness of the participants, even if it cannot ensure representativeness (Barberá & Zeitzoff, 2018 ; Hoblingre Klein, 2018 ). This point seems important because very homogenous samples were used in previous studies, especially with regard to professions.

Participants were volunteers, and were given the option of being able to stop the survey at any time. They received no compensation and no individual feedback. Participants were informed of these conditions before filling out the questionnaire. Oral and informed consent was obtained from all participants. Moreover, the Luxembourg Agency for Research Integrity (LARI on which the researchers in this study depend) specified that according to Code de la santé publique—Article L1123-7, it appears that France does not require research ethics committee [Les Comités de Protection des Personnes (CPP)] approval if the research is non-biomedical, non-interventional, observational, and does not collect personal health information, and thus CNR approval was not required.

Participants had to answer each question in order to submit the questionnaire: If one item was not answered, the respondent was not allowed to proceed to the next question. Thus, the database has no missing data. An introduction presented the subject of the study and its goals and guaranteed the participant’s anonymity. Researchers’ e-mail addresses were given, and participants were informed that they could contact the researchers for more information.

Data analyses

Three sets of statistical analyses were run on the data:

• Analysis of the items, using traditional true score theory and item response theory, for verifying the psychometric qualities (using mainly R package “psych”). The main objectives of this part of analysis were to better understand the variability of respondents’ answers, to compute the discriminatory power of items, and to verify the distribution of items by using every classical descriptive indicator (mean, standard-deviation, skewness, and kurtosis), corrected item-total correlations, and functions of responses for distributions.
• An exploratory factor analysis (EFA) with an oblimin rotation in order to define the latent structure of the meaning of work questionnaire, performed with the R packages “psych” and “GPArotation”. The structure we retained was based on adequation fits of various solutions (TLI, RMSEA and SRMR, see “List of abbreviations” section at the end of the article), and the use of R package “EFAtools” which helps to determine the adequate number of factors to retain for the EFA solution. Finally, this part of the analysis was concluded using calculations of internal consistency for each factor found in the scale.
• A confirmatory factor analysis using the R package Lavaan and based on the results of the EFA, in order to verify that the latent structure revealed in Step c was valid and relevant for this meaning of work scale. The adequation between data and latent structure was appreciated on the basis of CFI, TLI, RMSEA, and SRMR (see “Abbreviations” section).

For step a, the responses of the complete sample were considered. For steps b and c, 183 subjects were selected randomly for each analysis from the total study sample. Thus, two subsamples comprised of completely different participants were used, one for the EFA in step b and one for the CFA in step c.

Because of the ordinal measurement of the responses and its small number of categories (5-point Likert), none of the items can be normally distributed. This point was verified in step a of the analyses. Thus, the data did not meet the necessary assumptions for applying factor analyses with conventional estimators such as maximum likelihood (Li, 2015 ; Lubke & Muthén, 2004 ). Therefore, because the variables were measured on ordinal scales, it was most appropriate to apply the EFA and CFA analyses to the polychoric correlation matrix (Carroll, 1961 ). Then, to reduce the effects of the specific item distributions of the variables used in the factor analyses, a minimum residuals extraction (MINRES; Harman, 1960 ; Jöreskog, 2003 ) was used for the EFA, and a weighted least squares estimator with degrees of freedom adjusted for means and variances (WLSMV) was used for the CFA as recommended psychometric studies (Li, 2015 ; Muthén, 1984 ; Muthén & Kaplan, 1985 ; Muthén & Muthén, 2010 ; Yang, Nay, & Hoyle, 2010 ; Yu, 2002 ).

The size of samples for the different analyses has been taken into consideration. A model structure analysis with 30 observed variables needs a recommended minimum sample of 100 participants for 6 latent variables, and 200 for 5 latent variables (Soper, 2019 ). The samples used in the present research corresponded to these a priori calculations.

Finally, according to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), acceptable and excellent model fits are indicated by CFI and TLI values greater than .90 and .95, respectively, by RMSEA values smaller than .08 (acceptable) and .06 (excellent), respectively, and SRMR values smaller than .08.

Item analyses

The main finding was the limited amount of variability in the answers to each item. Indeed, as Table ​ Table4 4 shows, respondents usually and mainly chose the answers agree and strongly agree , as indicated by the column of cumulated percentages of these response modalities (%). Thus, for all items, the average answer was higher than 4, except for item 11, the median was 4, and skewness and kurtosis indicators confirmed a systematic skewed on the left leptokurtic distribution. This lack of variability in the participants’ responses and the high average scores indicate nearly unanimous agreement with the propositions made about the meaning of work in the questionnaire.

Distribution and analysis of the 30 items of the scale

M average of the answers to the item, SD standard deviation of the answers to the item, Med median, % cumulated percentages of answers 4 ( agree ) and 5 ( strongly agree ) for each item, skew skewness, kurt kurtosis, rit corrected item-total correlations

Table ​ Table4 4 also shows that the items had good discriminatory power, expressed by corrected item-total correlations (calculated with all items) which were above .40 for all items. Finally, item analyses were concluded through the application of item response theory (Excel tools using the eirt add in; Valois, Houssemand, Germain, & Belkacem, 2011 ) which confirmed, by analyses of item characteristic curves (taking into account that item response theory models are parametric and assume that the item responses distributions follow a logistic function, Rasch, 1980 ; Streiner, Norman, & Cairney, 2015 , p. 297), the psychometric quality of each item and their link to an identical latent dimension. These different results confirmed the interest in keeping all items of the questionnaire in order to measure the work-meaning construct.

Exploratory analyses of the scale

A five-factor solution was identified. This solution explained 58% of the total variance in the responses of the scale items; the TLI was .885, the RMSEA was .074, and the SRMR was .04. The structure revealed by this analysis was relatively simple (saturation of one main factor for each item; Thurstone, 1947 ), and the communality of each item was high, except for item 11. The solution we retained presented the best adequation fits and the most conceptual explanation concerning the latent factors. Additionally, the “EFAtools” R package confirmed the appropriateness of the chosen solution. Table ​ Table5 5 shows the EFA results, which described a five-factor structure.

Loadings and communalities of the 30 items from the meaning of work scale

EFA with five factors, oblimin rotation. Bold = loading ≥ .30. h 2 = communality

Nevertheless, the correlation matrix for the latent factors obtained by the EFA (see Table ​ Table6) 6 ) suggested the existence of a general second-order meaning of work factor, because the five factors were significantly correlated each with others. This result could be described as the existence of a general meaning of work factor, which alone would explain 44% of the total variance in the responses.

Correlations between the latent factors from the EFA, Cronbach’s alpha, and McDonald omega for each dimension and general factor

F1: success and recognition at work and from work; F2: usefulness; F3: respect; F4: value from and through work; F5: remuneration; general: total scale

Internal consistency of latent factors of the scale

The internal consistency of each latent factor, estimated by Cronbach alpha and McDonald omega, was high (above .80) and very high for the entire scale (α = .96 and ω = .97). Thus, for S uccess and Recognition at work and from work ’ s factor ω was .93, for Usefulness ’s factor ω was .92, for Respect ’s factor ω was .91, for Value from and through work ’s factor ω was slightly lower and equal to .85, and finally for Remuneration ’ s factor for which ω was .87.

Confirmatory factor analyses of the scale

In order to improve the questionnaire, we applied a CFA to this five-factor model to improve the model fit and refine the latent dimensions of the questionnaire. We used CFA to (a) determine the relevance of this latent five-factor structure and (b) confirm the relevance of a general second-order meaning-of-work factor. Although this procedure might appear redundant at first glance, it enabled us to select a definitive latent structure in which each item represents only one latent factor (simple structure; Thurstone, 1947 ), whereas the EFA that was computed in the previous step showed that certain items loaded on several factors. The CFA also easily verified the existence of a second-order latent meaning of work factor (the first-order loadings were .894, .920, .873, .892, and .918, respectively). Thus, this CFA was computed to complement the previous analyses by refining the latent model proposed for the questionnaire.

According to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), although the RMSEA value for the five-factor model was somewhat too high, the CFI and TLI values were excellent (χ 2 = 864.72, df = 400, RMSEA = .080, CFI = .989, TLI = .988). Table ​ Table7 7 presents the adequation fits for both solutions: a model with 5 first-order factors (as EFA suggests), and a model with 5 first-order factors and 1 second-order factor.

Solutions of confirmatory factor analyses

χ 2 Chi-square, df degrees of freedom, CFI comparative fit index, TLI Tucker-Lewis Index of factoring reliability, RMSEA root mean square error of approximation, SRMR standardized root mean square residual

Figure ​ Figure1 1 shows the model after the confirmatory test. This analysis confirmed the existence of a simple structure with five factors for the meaning of work scale and with a general, second-order factor of the meaning of work as suggested by the previous EFA.

Standardized solution of the structural model of the Meaning of Work Scale

The objective of this study was to verify the theoretical and psychometric structure of the meaning of work scale developed by Morin in recent years (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ). This scale has the advantages of being rather short, of proposing a multidimensional structure for the meaning of work, and of making it possible to assess the coherence between the aspects of work that are personally valued and the actual characteristics of the work environment. Thus, it can be used diagnostically or to guide individuals. To establish the structure of this scale, we analyzed deeply the items, and we implemented exploratory and confirmatory factor analyses, which we believe the scale’s authors had not carried out sufficiently. Moreover, we used a broad range of psychometric evaluation methods (traditional true score theory, item response theory, EFA, and structural equation modeling) to test the validity of the scale.

Item analyses confirmed results found in previous studies in which the meaning-of-work scale was administered. The majority of respondents agreed with the proposals of the questionnaire. Thus, this lack of variability is not specific to the present research and its sample (e.g., Morin & Cherré, 2004 ). Nevertheless, this finding can be explained by different reasons (which could be studied by other research) such as social desirability and the importance of work norms in industrial societies, or a lack of control regarding response bias.

The various versions of the latent structure of the scale proposed by the authors were not confirmed by the statistical analyses seen here. It nevertheless appears that this tool for assessing the meaning of work can describe and measure five different dimensions, all attached to a general factor. The first factor (F1), composed of nine items, is a dimension of recognition and success (e.g., item 17: work where your skills are recognized ; item 19: work where your results are recognized ; item 24: work that enables you to achieve the goals that you set for yourself ). It should thus be named Success and Recognition at work and from work and is comparable to dimensions from previous studies (personal success, Morin et al., 2001 ; social influence, Morin & Cherré, 2004 ). The second factor (F2), composed of seven items, is a dimension that represents the usefulness of work for an individual, whether that usefulness is social (e.g., Item 22: work that gives you the opportunity to serve others ) or personal (e.g., Item 28: work that enables you to be fulfilled ). It can be interpreted in terms of the Usefulness of work and generally corresponds to dimensions of the same name in earlier models (Morin, 2003 ; Morin & Cherré, 2004 ), although the definition used here is more precise. The third factor (F3), described by four items, refers to the Respect dimension of work (e.g., Item 5: work that respects human values ) and corresponds in part to the factors highlighted in prior studies (respect and rationalization of work, Morin, 2003 ; Morin & Cherré, 2004 ). The fourth factor (F4), composed of four items, refers to the personal development dimension and Value from and through work (e.g., Item 2: work that enables you to learn or to improve ). It is in some ways similar to autonomy and effectiveness, described by the authors of the scale (Morin, 2003 ; Morin & Cherré, 2004 ). Finally, the fifth and final factor (F5), with six items, highlights the financial and, more important, personal benefits sought or received from work. This includes physical and material safety and the enjoyment of work (e.g., item 14: work you enjoy doing ). This dimension of Remuneration partially converges with the aspects of personal values related to work described in previous research (Morin et al., 2001 ). Although the structure of the scale highlighted here differed from previous studies, some theoretical elements were nevertheless consistent with each other. To be convinced of this, the Table ​ Table8 8 highlights possible overlaps.

Final structure the items of the meaning of work scale by Morin and their theoretical dimensions

1 = Previous dimensions of Morin et al. ( 2001 ) and Morin ( 2003 ). 2 = Morin and Cherré ( 1999 )

A second important result of this study is the highlighting of a second-order factor by the statistical analyses carried out. This latent second-level factor refers to the existence of a general meaning of work dimension. This unitary conception of the meaning of work, subdivided into different linked facets, is not in contradiction with the different theories related to this construct. Thus, Ros et al. ( 1999 ) defined the meaning of work as a personal interpretation of experiences and interaction at work. This view of meaning of work can confer it a unitary functionality for maintaining psychological harmony, despite the destabilizing events that are often a feature of work. It must be considered as a permanent process of work adjustment or work adaptation. In order to be effective, this adjustment needs to remain consistent and to be globally oriented toward the cognitive balance between the reality of work and the meaning attributed to it. Thus, it has to keep a certain coherence which would explain the unitary conception of the meaning of work.

In addition to the purely statistical results of this study, whereas some partial overlap was found between the structural model in this study and structural models from previous work, this paper provides a much-needed updating and improvement of these dimensions, as we examined several theoretical meaning of work models in order to explain them psychologically. Indeed, the dimensions defined here as Success and Recognition , Usefulness , Respect , Value , and Remuneration from the meaning of work scale by Morin et al. ( 2001 ) have some strong similarities to other theoretical models on the meaning of work, even though the authors of the scale referred to these models only briefly. For example, the dimensions work centrality as a life role , societal norms regarding work , valued work outcomes , importance of work goals , and work-role identification (MOW International Research team, 1987 ) concur with the model described in the present study. In the same manner, the model by Rosso et al. ( 2010 ) has some similarities to the present structure, and there is a conceptual correspondence between the five dimensions found here and those from their study ( individuation , contribution , self-connection , and unification ). Finally, Baumeister’s ( 1991 ), Morin and Cherré’s ( 2004 ), and Sommer, Baumeister, and Stillman ( 2012 ) studies presented similar findings on the meaning of important life experiences for individuals; they described four essential needs that make such experiences coherent and reasonable ( purpose , efficacy - control , rectitude , and self - worth ). It is obvious that the parallels noted here were fostered by the conceptual breadth of the dimensions as defined in these models. In future research, much more precise definitions are needed. To do so, it will be essential to continue running analyses to test for construct validity by establishing convergent validity between the dimensions of the various existing meaning of work scales.

It is also interesting to note the proximity between the dimensions described here and those examined in studies on the dimensions that characterize the work context (Pignault & Houssemand, 2016 ) or in Karasek’s ( 1979 ) and Siegrist’s ( 1996 ) well-known models, for example, which determined the impact of work on health, stress, and well-being. These studies were able to clearly show how dimensions related to autonomy, support, remuneration, and esteem either contribute to health or harm it. These dimensions, which give meaning to work in a manner that is similar to the dimensions highlighted in the current study (Recognition, Value, and Remuneration in particular), are also involved in health. Thus, it would be interesting to verify the relations between these dimensions and measures of work health.

Thus, the conceptual dimensions of the meaning of work, as defined by Morin ( 2003 ) and Morin and Cherré ( 1999 ), remained of strong theoretical importance even if, at the empirical level, the scale created on this basis did not correspond exactly. The present study has had the modest merit of showing this interest, and also of proposing a new structure of the facets of this general dimension. One of the major interests of this research can be found in the possible better interpretations that this scale will enable to make. As mentioned above, the Morin’s scale is very frequently used in practice (e.g., in state employment agencies or by Human Resources departments), and the divergent models of previous studies could lead to individual assessments of the meaning of work diverging, depending on the reading grid chosen. Showing that a certain similarity in the structures of the meaning of work exists, and that a general factor of the meaning of work could be considered, the results of the current research can contribute to more precise use of this tool.

At this stage and in conclusion, it may be interesting to consider the reasons for the variations between the structures of the scale highlighted by the different studies. There were obviously the different changes applied to the different versions of the scale, but beyond that, three types of explanation could emerge. At the level of methods, the statistics used by the studies varied greatly, and could explain the variations observed. At the level of the respondents, work remains one of the most important elements of life in our societies. A certain temptation to overvalue its importance and purposes could be at the origin of the broad acceptance of all the proposals of the questionnaire, and the strong interactions between the sub-dimensions. Finally, at the theoretical level, if, as our study showed, a general dimension of meaning of work seems to exist, all the items, all the facets and all the first order factors of the scale, are strongly interrelated at each respective level. As well, small variations in the distribution of responses could lead to variations of the structure.

The principal contribution of this study is undoubtedly the use of confirmatory methods to test the descriptive models that were based on Morin’s scale (Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ). The principal results confirm that the great amount of interest in this scale is not without merit and suggest its validity for use in research, both by practitioners (e.g., career counselors and Human Resources departments) and diagnostically. The results show a tool that assesses a general dimension and five subdimensions of the meaning of work with a 30-item questionnaire that has strong psychometric qualities. Conceptual differences from previous exploratory studies were brought to light, even though there were also certain similarities. Thus, the objectives of this study were met.

Limitations

As with any research, this study also has a certain number of limitations. The first is the sample size used for statistical analyses. Even if the research design respected the general criteria for these kind of analyses (Soper, 2019 ), it will be necessary to repeat the study with larger samples. The second is the cultural and social character of the meaning of work, which was not addressed in this study because the sample was comprised of people working in France. They can thus be compared with those in Morin’s studies ( 2003 ) because of the linguistic proximity (French) of the samples, but differences in the structure of the scale could be due to cultural differences between America and Europe. Nevertheless, other different international populations should be questioned about their conception of the meaning of work in order to measure the impact of cultural and social aspects (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ). In the same vein, a third limitation involves the homogeneity of the respondents’ answers. Indeed, there was quasi-unanimous agreement with all of the items describing work (see Table ​ Table4 4 and previous results, Morin & Cherré, 2004 ). It is worth examining whether this lack of variance results from a work norm that is central and promoted in industrialized countries as it might mask broader interindividual differences. Thus, this study’s protocol should be repeated with other samples from different cultures. Finally, a fourth limitation that was mentioned previously involves the validity of the scale. Concerning the content validity and because some items loaded similarly different factors, it could be interesting to verify the wording content of the items, and potentially modify or replace some of them. The purpose of the present study was not to change the content of the scale but to suggest how future studies could analyze this point. Concerning the construct validity, this first phase of validation needs to be followed by other phases that involve tests of convergent validity between the existing meaning of work scales as well as tests of discriminant validity in order to confirm the existence of the meaning of work construct examined here. In such studies, the centrality of work (Warr, 2008 ; Warr, Cook, & Wall, 1979 ) should be used to confirm the validity of the meaning of work scale. Other differential, individual, and psychological variables related to work (e.g., performance, motivation, well-being) should also be introduced in order to expand the understanding of whether relations exist between the set of psychological concepts involved in work and individuals’ jobs.

Acknowledgements

Not applicable.

Abbreviations

Authors’ contributions

Both the authors are responsible for study conceptualization, data collection, data preparation, data analysis and report writing. The original questionnaire is a public one. No permission is required. The author(s) read and approved the final manuscript.

No funding.

Availability of data and materials

Ethics approval and consent to participate.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The Luxembourg Agency for Research Integrity (LARI) specifies that according to Code de la santé publique - Article L1123-7, it appears that France does not require research ethics committee (Les Comités de Protection des Personnes (CPP)) approval if the research is non-biomedical, non-interventional, observational, and does not collect personal health information. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. At the beginning of the questionnaire, the participants had to give their consent that the data could be used for research purposes, and they had to consent to the publication of the results of the study. Participation was voluntary and confidential. No potentially identifiable human images or data is presented in this study.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Anne Pignault, Email: [email protected] .

Claude Houssemand, Email: [email protected] .

• Aronsson G, Bejerot E, Häremstam A. Healthy work: Ideal and reality among public and private employed academics in Sweden. Personal Public Management. 1999; 28 (2):197–215. doi: 10.1177/009102609902800203. [ CrossRef ] [ Google Scholar ]
• Barberá P, Zeitzoff T. The new public address system: why do world leaders adopt social media? International Studies Quarterly. 2018; 62 (1):121–130. doi: 10.1093/isq/sqx047. [ CrossRef ] [ Google Scholar ]
• Baumeister RF. Meaning of life. New York: Guilford; 1991. [ Google Scholar ]
• Bendassolli PF, Borges-Andrade JE. Meaningfulness in work in Brazilian and French creative industries. Spanish Journal of Psychology. 2013; 16 :1–15. doi: 10.1017/sjp.2013.107. [ CrossRef ] [ Google Scholar ]
• Bendassolli PF, Borges-Andrade JE, Coelho Alves JS, de Lucena Torres T. Meaningful work scale in creative industries: A confirmatory factor analysis. Psico-USF. 2015; 20 (1):1–12. doi: 10.1590/1413-82712015200101. [ CrossRef ] [ Google Scholar ]
• Brief AP. MOW revisited: A brief commentary. European Work and Organizational Psychology. 1991; 1 :176–182. doi: 10.1080/09602009108408523. [ CrossRef ] [ Google Scholar ]
• Brief AP, Nord WR. Meaning of occupational work. Toronto: Lexington Books; 1990. [ Google Scholar ]
• Carroll JB. The nature of the data, or how to choose a correlation coefficient. Psychometrika. 1961; 26 :247–272. doi: 10.1007/bf02289768. [ CrossRef ] [ Google Scholar ]
• Dawis RV, Lofquist LH, Weiss DJ. A theory of work adjustment (a revision) Minnesota Studies in Vocational Rehabilitation, XXIII. 1968; 47 :1–14. doi: 10.1016/b978-0-08-013391-1.50030-4. [ CrossRef ] [ Google Scholar ]
• England, G. W. (1991). The meaning of working in USA: Recent changes. The European Work and Organizational Psychologist , 1 , 111–124.  10.1111/j.1464-0597.1990.tb01036.x 
• England GW, Harpaz I. How working is defined: National contexts and demographic and organizational role influences. Journal of Organizational Behavior. 1990; 11 :253–266. doi: 10.1002/job.4030110402. [ CrossRef ] [ Google Scholar ]
• Hackman JR, Oldham GR. Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance. 1976; 16 :250–279. doi: 10.1016/0030-5073(76)90016-7. [ CrossRef ] [ Google Scholar ]
• Harman HH. Modern Factor Analysis. Chicago: The University of Chicago Press; 1960. [ Google Scholar ]
• Harpaz I. The factorial structure of the meaning of work. Human Relations. 1986; 39 :595–614. doi: 10.1177/001872678603900701. [ CrossRef ] [ Google Scholar ]
• Harpaz I, Fu X. The structure of the meaning of work: A relative stability amidst change. Human Relations. 2002; 55 :639–−668. doi: 10.1177/0018726702556002. [ CrossRef ] [ Google Scholar ]
• Harpaz I, Meshoulam I. The meaning of work, employment relations, and strategic human resources management in Israel. Human Resource Management Review. 2010; 20 :212–223. doi: 10.1016/j.hrmr.2009.08.009. [ CrossRef ] [ Google Scholar ]
• Heider F. Attitudes and cognitive organization. Journal of Psychology. 1946; 21 :107–112. doi: 10.1080/00223980.1946.9917275. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hoblingre Klein H. Réseaux sociaux professionnels: instruments d’empowerment professionnel ?: Analyse de cas de consultants RH et de recruteurs sur LinkedIn. 2018. [ Google Scholar ]
• Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999; 6 :1–55. doi: 10.1080/10705519909540118. [ CrossRef ] [ Google Scholar ]
• Jöreskog KG. Factor analysis by MINRES. 2003. [ Google Scholar ]
• Karasek RA. Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly. 1979; 24 :285–308. doi: 10.2307/2392498. [ CrossRef ] [ Google Scholar ]
• Ketchum LD, Trist E. All teams are not created equal. How employee empowerment really works. Newbury Park: Sage; 1992. [ Google Scholar ]
• Kline RB. Principles and practices of structural equation modeling. 3. New-York: Guilford; 2011. [ Google Scholar ]
• Li, C. H. (2015). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood diagonally weighted least squares. Behavior Research Method , 1–14. 10.3758/s13428-015-0619-7. [ PubMed ]
• Lips-Wiersma M, Wright S. Measuring the meaning of meaningful work: Development and validation of the Comprehensive Meaningful Work Scale (CMWS) Group & Organization Management. 2012; 37 (5):655–685. doi: 10.1177/1059601112461578. [ CrossRef ] [ Google Scholar ]
• Lubke GH, Muthén BO. Applying multigroup confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons. Structural Equation Modeling. 2004; 11 :514–534. doi: 10.1207/s15328007sem1104_2. [ CrossRef ] [ Google Scholar ]
• McKee-Ryan F, Song Z, Wanberg CR, Kinicki AJ. Psychological and physical well-being during unemployment: A meta- analytic study. Journal of Applied Psychology. 2005; 90 :53–76. doi: 10.1037/0021-9010.90.1.53. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morin E. Sens du travail. Définition, mesure et validation. In: Vandenberghe C, Delobbe N, Karnas G, editors. Dimensions individuelles et sociales de l’investissement professionnel. Louvain: Presses Universitaires de Louvain; 2003. pp. 11–20. [ Google Scholar ]
• Morin E. Document—Centre de recherche et d’intervention pour le travail, l’efficacité organisationnelle et la santé (CRITEOS), HEC Montréal. 2006. Donner un sens au travail. [ Google Scholar ]
• Morin E. Sens du travail, santé mentale et engagement organisationnel. Montréal: IRSST; 2008. [ Google Scholar ]
• Morin E, Archambault M, Giroux H. Projet Qualité de Vie au Travail. Rapport Final. Montréal: HEC Montréal; 2001. [ Google Scholar ]
• Morin E, Cherré B. Les cadres face au sens du travail. Revue Française de Gestion. 1999; 126 :83–93. doi: 10.3166/rfg.251.149-164. [ CrossRef ] [ Google Scholar ]
• Morin E, Cherré B. Réorganiser le travail et lui donner du sens. In: Lancry A, Lemoine C, editors. La personne et ses rapports au travail. Paris: L’Harmattan; 2004. pp. 87–102. [ Google Scholar ]
• Morse NC, Weiss RS. The function and meaning of work and the job. American Sociological Review. 1955; 20 :191–198. doi: 10.2307/2088325. [ CrossRef ] [ Google Scholar ]
• MOW International Research team . The meaning of working. London: Academic Press; 1987. [ Google Scholar ]
• Muthén B. A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika. 1984; 49 :115–132. doi: 10.1007/bf02294210. [ CrossRef ] [ Google Scholar ]
• Muthén LK, Kaplan D. A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology. 1985; 38 :171–189. doi: 10.1111/j.2044-8317.1985.tb00832.x. [ CrossRef ] [ Google Scholar ]
• Muthén LK, Muthén BO. Mplus user’s guide. 6. Los Angeles: Muthén & Muthén; 2010. [ Google Scholar ]
• Osborne, J. W. & Fitzpatrick, D. C. (2012). Replication analysis in exploratory factor analysis: what it is and why it makes your analysis better. Practical Assessment, Research & Evaluation , 17 (15), 1–8. 10.7275/h0bd-4d11.
• Osgood CE, Tannenbaum PH. The principle of congruity in the perception of attitude change. Psychological Review. 1955; 62 :42–55. doi: 10.1037/h0048153. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pignault A, Houssemand C. Construction and initial validation of the Work Context Inventory. Journal of Vocational Behavior. 2016; 92 :1–11. doi: 10.1016/j.jvb.2015.11.006. [ CrossRef ] [ Google Scholar ]
• Pratt MG, Ashforth BE. Fostering meaningfulness in working and at work. In: Cameron KS, Dutton JE, Quinn RE, editors. Positive organizational scholarship. San Francisco: Berrett-Koehler Publishers; 2003. pp. 309–327. [ Google Scholar ]
• Rasch G. Probabilistic models for some intelligence and attainment tests. Chicago: The University of Chicago Press; 1980. [ Google Scholar ]
• Roe RA, Ester P. Values and Work: Empirical findings and theoretical perspective. Applied Psychology: An international review. 1999; 48 (1):1–21. doi: 10.1111/j.1464-0597.1999.tb00046.x. [ CrossRef ] [ Google Scholar ]
• Ros M, Schwartz SH, Surkiss S. Basic individual values, work values, and meaning of work. Applied Psychology: An international review. 1999; 48 (1):49–71. doi: 10.1111/j.1464-0597.1999.tb00048.x. [ CrossRef ] [ Google Scholar ]
• Rosso BD, Dekas KH, Wrzesniewski A. On the meaning of work: A theoretical integration and review. Research in Organizational Behavior. 2010; 30 :91–127. doi: 10.1016/j.riob.2010.09.001. [ CrossRef ] [ Google Scholar ]
• Ruiz-Quintanilla SA, England GW. How working is defined: Structure and stability. 1994. [ Google Scholar ]
• Siegrist JA. Adverse health effects of high-effort/low-reward conditions. Journal of Occupational Health Psychology. 1996; 1 :27–41. doi: 10.1037/1076-8998.1.1.27. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sommer KL, Baumeister RF, Stillman TF. The construction of meaning from life events: Empirical studies of personal narratives. In: Wong PTP, editor. The Human Quest for Meaning. New York: Routledge; 2012. pp. 297–314. [ Google Scholar ]
• Soper DS. A-priori sample size calculator for structural equation models [Software] 2019. [ Google Scholar ]
• Steers RM, Porter L. Work and motivation: An evaluative summary. In: Steers RM, Porter L, editors. Motivation and work behaviour. New-York: McGraw-Hill; 1979. pp. 555–564. [ Google Scholar ]
• Steger MF, Dik BJ, Duffy RD. Measuring meaningful work: The work and meaning inventory (WAMI) Journal of Career Assessment. 2012; 20 (3):322–337. doi: 10.1177/1069072711436160. [ CrossRef ] [ Google Scholar ]
• Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health measurement scales. Oxford Medicine Online. 10.1093/med/9780199685219.001.0001.
• Super DE, Sverko B. Life roles, values, and careers. San Francisco: Jossey-Bass Publishers; 1995. [ Google Scholar ]
• Sverko B. Origin of individual differences in importance attached to work: A model and a contribution to its evaluation. Journal of Vocational Behavior. 1989; 34 :28–39. doi: 10.1016/0001-8791(89)90062-6. [ CrossRef ] [ Google Scholar ]
• Sverko B, Vizek-Vidovic V. Studies of the meaning of work: Approaches, models, models and some of the findings. In: Super DE, Sverko B, editors. Life roles, values, and Careers. San Francisco: Jossey-Bass Publishers; 1995. pp. 3–21. [ Google Scholar ]
• Thurstone LL. Multiple-factor analysis. Chicago: University of Chicago Press; 1947. [ Google Scholar ]
• Topalova V. Changes in the attitude to work and unemployment during the period of social transition. In: Roe RA, Russinova V, editors. Psychosocial aspects of employment: European perspectives. Tilburg: Tilburg University Press; 1994. pp. 21–28. [ Google Scholar ]
• Valois P, Houssemand C, Germain S, Belkacem A. An open source tool to verify the psychometric properties of an evaluation instrument. Procedia Social and Behavioral Sciences. 2011; 15 :552–556. doi: 10.1016/j.sbspro.2011.03.140. [ CrossRef ] [ Google Scholar ]
• Wanberg, C.R. (2012) The Individual Experience of Unemployment. Annual Review of Psychology, 63 (1), 369-396. 10.1146/annurev-psych-120710-100500 [ PubMed ]
• Warr P. Work values: Some demographic and cultural correlates. Journal of Occupational and Organizational Psychology. 2008; 81 :751–775. doi: 10.1348/096317907x263638. [ CrossRef ] [ Google Scholar ]
• Warr PB, Cook JD, Wall TD. Scales of measurement of some work attitudes and aspects of psychological well-being. Journal of Occupational Psychology. 1979; 52 :129–148. doi: 10.1111/j.2044-8325.1979.tb00448.x. [ CrossRef ] [ Google Scholar ]
• Wrzesniewski A, Dutton JE, Debebe G. Interpersonal sensemaking and the meaning of work. Research in Organizational Behavior. 2003; 25 :93–135. doi: 10.1016/s0191-3085(03)25003-6. [ CrossRef ] [ Google Scholar ]
• Wrzesniewski A, McCauley C, Rozin P, Schwartz B. Jobs, careers, and callings: people’s relations to their work. Journal of Research in Personality. 1997; 31 :21–33. doi: 10.1006/jrpe.1997.2162. [ CrossRef ] [ Google Scholar ]
• Yang C, Nay S, Hoyle RH. Three approaches to using lengthy ordinal scales in structural equation models. Parceling, latent scoring, and shortening scales. Applied Psychological Measurement. 2010; 34 :122–142. doi: 10.1177/0146621609338592. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yu C-Y. Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Dissertation. Los Angeles: University of California; 2002. [ Google Scholar ]
• Zanders H. Changing work values. In: Ester P, Halman L, de Moor R, editors. The individualizing society. Value changes in Europe and North-America. Tilburg: Tilburg University Press; 1993. pp. 129–153. [ Google Scholar ]

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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* Research Basics *

• Introduction

So What Do We Mean By “Formal Research?”

• Guide License
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A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

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• Last Updated: Jul 3, 2024 12:55 PM
• URL: https://guides.library.iit.edu/research_basics

What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

Agnes, M., & Guralnik, D. B. (Eds.). (2008). Hypothesis. In Webster’s new world college dictionary (4th ed.). Wiley.

Google Scholar  

Britannica. (n.d.). Scientific method. In Encyclopaedia Britannica . Retrieved July 15, 2022 from https://www.britannica.com/science/scientific-method

Brownell, W. A., & Moser, H. E. (1949). Meaningful vs. mechanical learning: A study in grade III subtraction . Duke University Press..

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019b). Posing significant research questions. Journal for Research in Mathematics Education, 50 (2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114

Article   Google Scholar  

Cambridge University Press. (n.d.). Hypothesis. In Cambridge dictionary . Retrieved July 15, 2022 from https://dictionary.cambridge.org/us/dictionary/english/hypothesis

Cronbach, J. L. (1957). The two disciplines of scientific psychology. American Psychologist, 12 , 671–684.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30 , 116–127.

Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Shweder (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 83–107). University of Chicago Press.

Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

Merriam-Webster. (n.d.). Explain. In Merriam-Webster.com dictionary . Retrieved July 15, 2022, from https://www.merriam-webster.com/dictionary/explain

National Research Council. (2002). Scientific research in education . National Academy Press.

Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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research and development , in industry , two intimately related processes by which new products and new forms of old products are brought into being through technological innovation .

Research and development, a phrase unheard of in the early part of the 20th century, has since become a universal watchword in industrialized nations. The concept of research is as old as science; the concept of the intimate relationship between research and subsequent development, however, was not generally recognized until the 1950s. Research and development is the beginning of most systems of industrial production. The innovations that result in new products and new processes usually have their roots in research and have followed a path from laboratory idea, through pilot or prototype production and manufacturing start-up, to full-scale production and market introduction. The foundation of any innovation is an invention . Indeed, an innovation might be defined as the application of an invention to a significant market need. Inventions come from research—careful, focused, sustained inquiry, frequently trial and error. Research can be either basic or applied, a distinction that was established in the first half of the 20th century.

Basic research is defined as the work of scientists and others who pursue their investigations without conscious goals, other than the desire to unravel the secrets of nature. In modern programs of industrial research and development, basic research (sometimes called pure research) is usually not entirely “pure”; it is commonly directed toward a generalized goal, such as the investigation of a frontier of technology that promises to address the problems of a given industry. An example of this is the research being done on gene splicing or cloning in pharmaceutical company laboratories.

Applied research carries the findings of basic research to a point where they can be exploited to meet a specific need, while the development stage of research and development includes the steps necessary to bring a new or modified product or process into production. In Europe , the United States , and Japan the unified concept of research and development has been an integral part of economic planning , both by government and by private industry.

The first organized attempt to harness scientific skill to communal needs took place in the 1790s, when the young revolutionary government in France was defending itself against most of the rest of Europe. The results were remarkable. Explosive shells, the semaphore telegraph, the captive observation balloon, and the first method of making gunpowder with consistent properties all were developed during this period.

The lesson was not learned permanently, however, and another half century was to pass before industry started to call on the services of scientists to any serious extent. At first the scientists consisted of only a few gifted individuals. Robert W. Bunsen, in Germany, advised on the design of blast furnaces. William H. Perkin, in England, showed how dyes could be synthesized in the laboratory and then in the factory. William Thomson (Lord Kelvin), in Scotland, supervised the manufacture of telecommunication cables. In the United States, Leo H. Baekeland, a Belgian, produced Bakelite, the first of the plastics. There were inventors, too, such as John B. Dunlop, Samuel Morse, and Alexander Graham Bell , who owed their success more to intuition , skill, and commercial acumen than to scientific understanding.

While industry in the United States and most of western Europe was still feeding on the ideas of isolated individuals, in Germany a carefully planned effort was being mounted to exploit the opportunities that scientific advances made possible. Siemens, Krupp, Zeiss, and others were establishing laboratories and, as early as 1900, employed several hundred people on scientific research. In 1870 the Physicalische Technische Reichsanstalt (Imperial Institute of Physics and Technology) was set up to establish common standards of measurement throughout German industry. It was followed by the Kaiser Wilhelm Gesellschaft (later renamed the Max Planck Society for the Advancement of Science), which provided facilities for scientific cooperation between companies.

In the United States, the Cambria Iron Company set up a small laboratory in 1867, as did the Pennsylvania Railroad in 1875. The first case of a laboratory that spent a significant part of its parent company’s revenues was that of the Edison Electric Light Company, which employed a staff of 20 in 1878. The U.S. National Bureau of Standards was established in 1901, 31 years after its German counterpart, and it was not until the years immediately preceding World War I that the major American companies started to take research seriously. It was in this period that General Electric , Du Pont, American Telephone & Telegraph, Westinghouse, Eastman Kodak, and Standard Oil set up laboratories for the first time.

Except for Germany, progress in Europe was even slower. When the National Physical Laboratory was founded in England in 1900, there was considerable public comment on the danger to Britain’s economic position of German dominance in industrial research, but there was little action. Even in France, which had an outstanding record in pure science , industrial penetration was negligible.

World War I produced a dramatic change. Attempts at rapid expansion of the arms industry in the belligerent as well as in most of the neutral countries exposed weaknesses in technology as well as in organization and brought an immediate appreciation of the need for more scientific support. The Department of Scientific and Industrial Research in the United Kingdom was founded in 1915, and the National Research Council in the United States in 1916. These bodies were given the task of stimulating and coordinating the scientific support to the war effort, and one of their most important long-term achievements was to convince industrialists, in their own countries and in others, that adequate and properly conducted research and development were essential to success.

At the end of the war the larger companies in all the industrialized countries embarked on ambitious plans to establish laboratories of their own; and, in spite of the inevitable confusion in the control of activities that were novel to most of the participants, there followed a decade of remarkable technical progress. The automobile, the airplane, the radio receiver, the long-distance telephone, and many other inventions developed from temperamental toys into reliable and efficient mechanisms in this period. The widespread improvement in industrial efficiency produced by this first major injection of scientific effort went far to offset the deteriorating financial and economic situation.

The economic pressures on industry created by the Great Depression reached crisis levels by the early 1930s, and the major companies started to seek savings in their research and development expenditure. It was not until World War II that the level of effort in the United States and Britain returned to that of 1930. Over much of the European continent the depression had the same effect, and in many countries the course of the war prevented recovery after 1939. In Germany Nazi ideology tended to be hostile to basic scientific research, and effort was concentrated on short-term work.

The picture at the end of World War II provided sharp contrasts. In large parts of Europe industry had been devastated, but the United States was immensely stronger than ever before. At the same time the brilliant achievements of the men who had produced radar, the atomic bomb , and the V-2 rocket had created a public awareness of the potential value of research that ensured it a major place in postwar plans. The only limit was set by the shortage of trained persons and the demands of academic and other forms of work.

Since 1945 the number of trained engineers and scientists in most industrial countries has increased each year. The U.S. effort has stressed aircraft, defense, space, electronics , and computers. Indirectly, U.S. industry in general has benefited from this work, a situation that compensates in part for the fact that in specifically nonmilitary areas the number of persons employed in the United States is lower in relation to population than in a number of other countries.

Outside the air, space, and defense fields the amount of effort in different industries follows much the same pattern in different countries, a fact made necessary by the demands of international competition. (An exception was the former Soviet Union , which devoted less R and D resources to nonmilitary programs than most other industrialized nations.) An important point is that countries like Japan, which have no significant aircraft or military space industries, have substantially more manpower available for use in the other sectors. The preeminence of Japan in consumer electronics, cameras, and motorcycles and its strong position in the world automobile market attest to the success of its efforts in product innovation and development.

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What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire

• Anne Pignault   ORCID: orcid.org/0000-0001-7946-3793 1 &
• Claude Houssemand 2  

Psicologia: Reflexão e Crítica volume  34 , Article number:  2 ( 2021 ) Cite this article

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Considering the recent and current evolution of work and the work context, the meaning of work is becoming an increasingly relevant topic in research in the social sciences and humanities, particularly in psychology. In order to understand and measure what contributes to the meaning of work, Morin constructed a 30-item questionnaire that has become predominant and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, it has been validated only in part.

Meaning of work questionnaire was conducted in French with 366 people (51.3% of women; age: ( M = 39.11, SD = 11.25); 99.2% of whom were employed with the remainder retired). Three sets of statistical analyses were run on the data. Exploratory and confirmatory factor analysis were conducted on independent samples.

The questionnaire described a five-factor structure. These dimensions (Success and Recognition at work and of work, α = .90; Usefulness, α = .88; Respect for work, α = .88; Value from and through work, α = .83; Remuneration, α = .85) are all attached to a general second-order latent meaning of work factor (α = .96).

Conclusions

Validation of the scale, and implications for health in the workplace and career counseling practices, are discussed.

Introduction

Since the end of the 1980s, many studies have been conducted to explore the meaning of work, particularly in psychology (Rosso, Dekas, & Wrzesniewski, 2010 ). A review of the bibliographical data in PsychInfo shows that between 1974 and 2006, 183 studies addressed this topic (Morin, 2006 ). This scholarly interest was primarily triggered by Sverko and Vizek-Vidovic’s ( 1995 ) article, which identified the approaches and models that have been used and their main results.

Whereas early studies on the meaning of work introduced the concept and its theoretical underpinnings (e.g., Harpaz, 1986 ; Harpaz & Fu, 2002 ; Morin, 2003 ; MOW International Research team, 1987 ), later research tried to connect this aspect of work with other psychological dimensions or individual perceptions of the work context (e.g., Harpaz & Meshoulam, 2010 ; Morin, 2008 ; Morin, Archambault, & Giroux, 2001 ; Rosso et al., 2010 ; Wrzesniewski, Dutton, & Debebe, 2003 ). Nevertheless, scholars, particularly those in organizational and occupational psychology, soon found it difficult to precisely identify the meaning of work because it changes in accordance with the conceptualizations of different researchers, the theoretical models used to describe it, and the tools that are available to measure it for individuals and for groups.

This article first seeks to clarify the concept of the meaning of work (definitions and models) before bringing up certain problems involved in its measurement and the diversity in how the concept has been used. Then the paper focuses on a particular meaning of work measurement tool developed in Canada, which is now widely used in French-speaking countries. At the beginning of the twenty-first century, Morin et al. ( 2001 ) developed a 30-item questionnaire to better determine the dimensions that give meaning to a person’s work. The statistical analyses needed to determine the reliability and validity of Morin et al.’s meaning of work questionnaire have never been completed. Indeed, some changes were made to the initial scale, and the analyses only based on homogenous samples of workers in different professional sectors. Thus and even though the meaning of work scale is used quite frequently, both researchers and practitioners have been unsure about whether or not to trust its results. The main objective of the present study was thus to provide a psychometric validation of Morin et al.’s meaning of work scale and to uncover its latent psychological structure.

Meaning of work: from definition to measurement

Meaning of work: what is it.

As many scholars have found, the concept of the meaning of work is not easy to define (e.g., Rosso et al., 2010 ). In terms of theory, it has been defined differently in different academic fields. In psychology, it refers to an individual’s interpretations of his/her actual experiences and interactions at work (Ros, Schwartz, & Surkiss, 1999 ). From a sociological point of view, it involves assessing meaning in reference to a system of values (Rosso et al., 2010 ). In this case, its definition depends on cultural or social differences, which make explaining this concept even more complex (e.g., Morse & Weiss, 1955 ; MOW International Research team, 1987 ; Steers & Porter, 1979 ; Sverko & Vizek-Vidovic, 1995 ).

At a conceptual level, the meaning of work has been defined in three different ways (Morin, 2003 ). First, it can refer to the meaning of work attached to an individual’s representations of work and the values he/she attributes to that work (Morse & Weiss, 1955 ; MOW International Research team, 1987 ). Second, it can refer to a personal preference for work as defined by the intentions that guide personal action (Super & Sverko, 1995 ). Third, it can be understood as consistency between oneself and one’s work, similar to a balance in one’s personal relationship with work (Morin & Cherré, 2004 ).

With respect to terms, some differences exist because the meaning of work is considered an individual’s interpretation of what work means or of the role it plays in one’s life (Pratt & Ashforth, 2003 ). Yet this individual perception is also influenced by the environment and the social context (Wrzesniewski et al., 2003 ). The psychological literature on the meaning of work has primarily examined its positive aspects, even though work experiences can be negative or neutral. This partiality about the nature of the meaning of work in research has led to some confusion in the literature between this concept and that of meaningful , which refers to the extent to which work has personal significance (a quantity) and seems to depend on positive elements (Steger, Dik, & Duffy, 2012 ). A clearer demarcation should be made between these terms in order to specify the exact sense of the meaning of work: “This would reserve ‘meaning’ for instances in which authors are referring to what work signifies (the type of meaning), rather than the amount of significance attached to the work” (Rosso et al., 2010 , p. 95).

The original idea of the meaning of work refers to the central importance of work for people, beyond the simple behavioral activity through which it occurs. Drawing on various historical references, certain authors present work as an essential driver of human life; these scholars then seek to understand how work is fundamental (e.g., Morin, 2006 ; Sverko & Vizek-Vidovic, 1995 ). The concept of the meaning of work is connected to the centrality of work for the individual and consequently fulfills four different important functions: economic (to earn a living), social (to interact with others), prestige (social position), and psychological (identity and recognition). In this view, the centrality of work is based on an ensemble of personal and social values that differ between individuals as well as between cultures, economic climates, and occupations (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ).

Meaning of work: which theoretical model?

The first theoretical model for the meaning of work was based on research in the MOW project (MOW International Research team, 1987 ), considered the “most empirically rigorous research ever undertaken to understand, both within and between countries, the meanings people attach to their work roles” (Brief, 1991 , p. 176). This view suggests that the meaning of work is based on five principal theoretical dimensions: work centrality as a life role, societal norms regarding work, valued work outcomes, importance of work goals, and work-role identification. A series of studies on this theory was conducted in Israel (Harpaz, 1986 ; Harpaz & Fu, 2002 ; Harpaz & Meshoulam, 2010 ), complementing the work of the MOW project (MOW International Research team, 1987 ). Harpaz ( 1986 ) empirically identified six latent factors that represent the meaning of work: work centrality, entitlement norm, obligation norm, economic orientation, interpersonal relations, and expressive orientation.

Another theoretical model on the importance of work in a person’s life was created by Sverko in 1989 . This approach takes into account the interactions among certain work values (the importance of these values and the perception of possible achievements through work), which depend on a process of socialization. The ensemble is then moderated by an individual’s personal experiences with work. In the same vein, Rosso et al. ( 2010 ) tried to create an exhaustive model of the sources that influence the meaning of work. This model is built around two major dimensions: Self-Others (individual vs. other individuals, groups, collectives, organizations, and higher powers) and Agency-Communion (the drives to differentiate, separate, assert, expand, master, and create vs. the drives to contact, attach, connect, and unite). This theoretical framework describes four major pathways to the meaning of work: individuation (autonomy, competence, and self-esteem), contribution (perceived impact, significance, interconnection, and self-abnegation), self-connection (self-concordance, identity affirmation, and personal engagement), and unification (value systems, social identification, and connectedness).

Lastly, a more recent model (Lips-Wiersma & Wright, 2012 ) converges with the theory suggested by Rosso et al. ( 2010 ) but distinguishes two dimensions: Self-Others versus Being-Doing. This model describes four pathways to meaningful work: developing the inner self, unity with others, service to others, and expressing one’s full potential.

Without claiming to be exhaustive, this brief presentation of the theoretical models of the meaning of work underscores the difficulty in precisely defining this concept, the diversity of possible approaches to identifying its contours, and therefore implicitly addresses the various tools designed to measure it.

Measuring the meaning of work

Various methodologies have been used to better determine the concept of the meaning of work and to grasp what it involves in practice. The tools examined below have been chosen because of their different methodological approaches.

One of the first kinds of measurements was developed by the international MOW project (MOW International Research team, 1987 ). In this study, England and Harpaz ( 1990 ) and Ruiz-Quintanilla and England ( 1994 ) used 14 defining elements to assess agreement on the perception of work of 11 different sample groups questioned between 1989 and 1992. These elements, resulting from the definition of work given by the MOW project and studied by applying multivariate analyses and textual content analyses ( When do you consider an activity as working ? Choose four statements from the list below which best define when an activity is “ working,” MOW International Research team, 1987 ), can be grouped into four distinct heuristic categories (Table 1 ).

Similarly, England ( 1991 ) studied changes in the meaning of work in the USA between 1982 and 1989. He used four different methodological approaches to the meaning of work: societal norms about work, importance of work goals, work centrality, and definition of work by the labor force. In the wake of these studies, others developed scales to measure the centrality of work in people’s lives, either for the general population (e.g., Warr, 2008 ) or for specific subpopulations such as unemployed people, on the basis of a rather similar conceptualization of the meaning of work (McKee-Ryan, Song, Wanberg, & Kinicki, 2005 ; Wanberg, 2012 ).

Finally, Wrzesniewski, McCauley, Rozin, and Schwartz ( 1997 ) developed a rather unusual method for evaluating people’s relationships with their work. Although not directly connected to research on the meaning of work, this study and the questionnaire they used ( University of Pennsylvania Work-Life Questionnaire ) addressed some of the same concepts. Above all, they employed the concepts in a very particular way that combined psychological scales, scenarios, and sociodemographic questions. Through these scenarios (Table 2 ) and the extent to which the respondents felt like the described characters, their relationship to work was described as either a Job, a Career, or a Calling.

This presentation of certain tools for measuring the meaning of work reveals a variety of methodological approaches. Nevertheless, whereas certain methods have adopted a rather traditional psychological approach, others are often difficult to use for various reasons such as their psychometrics (e.g., the use of only one item to measure a concept; England, 1991 ; Wrzesniewski et al., 1997 ) or for practical reasons (e.g., the participants were asked questions that pertained not only to their individual assessment of work but also to various other parts of their lives; England, 1991 ; Warr, 2008 ). This diversity in the possible uses of the meaning of work makes it difficult to select a tool to measure it.

In French-speaking countries (Canada and Europe primarily), the previously mentioned scale created by Morin et al. ( 2001 ) has predominated and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, there has not been a complete validation of the scale (i.e., different forms of the same tool, only the use of exploratory factor analyses, and no similar structures found) that was the motivation for the current study.

The present study

The present article conceives of the meaning of work as representing a certain consistency between what an individual wants out of work and the individual’s perception, lived or imagined, of his/her work. It thus corresponds to the third definition of the meaning of work presented above—consistency between oneself and one's work (Morin & Cherré, 2004 ). This definition is strictly limited to the meaning given to work and the personal significance of this work from the activities that the work implies. Within this conceptual framework, some older studies adopted a slightly different cognitive conception, in which individuals constantly seek a balance between themselves and their environment, and any imbalance triggers a readjustment through which the person attempts to stabilize his/her cognitive state (e.g., Heider, 1946 ; Osgood & Tannenbaum, 1955 ). Here, the meaning of work must be considered a means for maintaining psychological harmony despite the destabilizing events that work might involve. In this view, meaning is viewed as an effect or a product of the activity (Brief & Nord, 1990 ) and not as a permanent or fixed state. It then becomes a result of person-environment fit and falls within the theory of work adjustment (Dawis, Lofquist, & Weiss, 1968 ).

Within this framework, a series of recurring and interdependent studies should be noted (e.g., Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ) because they have attempted to measure the coherence that a person finds in the relation between the person’s self and his/her work and thus implicitly the meaning of that work. Therefore, these studies make it possible to understand the meaning of work in greater detail, meaning that it could be used in practice through a self-evaluation questionnaire. The level of coherence is considered the degree of similarity between the characteristics of work that the person attributes meaning to and the characteristics that he/she perceives in his/her present work (Aronsson, Bejerot, & Häremstam, 1999 ; Morin & Cherré, 2004 ). Based on semi-structured interviews and on older research related to the quality of life at work (Hackman & Oldham, 1976 ; Ketchum & Trist, 1992 ), a model involving 14 characteristics was developed: the usefulness of work, the social contribution of work, rationalization of the tasks, workload, cooperation, salary, the use of skills, learning opportunities, autonomy, responsibilities, rectitude of social and organizational practices, the spirit of service, working conditions, and, finally, recognition and appreciation (Morin, 2006 ; Morin & Cherré, 1999 ). Then, based on this model, a 30-item questionnaire was developed to offer more precise descriptions of these dimensions. Table 3 presents the items, which were designed and administered to the participants in French.

Some studies for structurally validating this questionnaire have been conducted over the years (e.g., Morin, 2003 , 2006 , 2008 ; Morin & Cherré, 2004 ). However, their results were not very precise or comparable. For example, the number of latent factors in the meaning of work scale structure varied (e.g., six or eight factors: Morin, 2003 ; six factors: Morin, 2006 ; Morin & Cherré, 2004 ), the sample groups were not completely comparable (especially with respect to occupations), and finally, items were added or removed or their phrasing was changed (e.g., 30 and 33 items: Morin, 2003 ; 30 items: Morin, 2006 ; 26 items: Morin, 2008 ). Yet the most prominent methodological problem was that only exploratory analyses (most often a principal component analysis with varimax rotation) had been applied. This scale was entirely relevant from a theoretical point of view because it offered a more specific definition of the meaning of work than other scales and, mainly, because some subdimensions appeared to be linked with anxiety, depression, irritability, cognitive problems, psychological distress, and subjective well-being (Morin et al., 2001 ). It was also relevant from a practical point of view because it was short and did not take much time to complete. However, its use was questionable because it had never been validated psychometrically, and a consistent latent psychological structure had not been identified across studies.

As an example, two models representing the structure of the 30-item scale are presented in Table 3 (Morin et al., 2001 ; Morin, 2003 , for the first model; Morin & Cherré, 2004 , for the second one). This table presents the items, the meaning of work dimensions they are theoretically related to, and the solution from the principal component analysis in each study. These analyses revealed that the empirical and theoretical structures of this tool are not stable and that the latent structure suffers from the insufficient use of statistical methods. In particular, there was an important difference found between the two models in previous studies (Morin et al., 2001 ; Morin & Cherré, 2004 ). Only the “usefulness of work” dimension was found to be identical, comprised of the same items in both models. Other dimensions had a maximum of only three items in common. Therefore, it is very difficult to utilize this tool both in practice and diagnostically, and complementary studies must be conducted. Even though there are techniques for replicating explanatory analyses (e.g., Osborne, 2012 ), such techniques could not be used here because not all the necessary information was given (e.g., all factor loadings, communalities). This is why collecting new data appeared to be the only way to analyze the scale.

More recently, two studies (which applied a new 25-item meaningful work questionnaire ) were developed on the basis of Morin’s scale (Bendassolli & Borges-Andrade, 2013 ; Bendassolli, Borges-Andrade, Coelho Alves, & de Lucena Torres, 2015 ). Even though the concepts of the “meaning of work” and “meaningful work” are close, the two scales are formally and theoretically different and do not evaluate the same construct.

The purpose of the present study was thus to determine the structure of original Morin’s 30-item scale (Morin, 2003 ; Morin & Cherré, 2004 ) by using an exploratory approach as well as confirmatory statistical methods (structural equation modeling) and in so doing, to address the lacunae in previous research discussed above. The end goal was thus to identify the structure of the scale statistically so that it can be used empirically in both academic and professional fields. Indeed, as mentioned previously, this scale is of particular interest to researchers because its design is not limited to measuring a general meaning of work for each individual; it can also be used to evaluate discrepancies or a convergence between a person’s own personal meaning of work and a specific work context (e.g., tasks, relations with others, autonomy). Finally, and with respect to previous results, the scale could be a potential predictor of professional well-being and psychological distress at work (Morin et al., 2001 ).

Participants

The questionnaire was conducted with 366 people who were mainly resident in Paris and the surrounding regions in France. The gender distribution was almost equal; 51.3% of the respondents were women. The respondents’ ages ranged from 19 to 76 years ( M = 39.11, SD = 11.25). The large majority of people were employed (99.2%). Twenty percent worked in medical and paramedical fields, 26% in retail and sales, and 17% in human resources (the other respondents worked in education, law, communication, reception, banking, and transportation). Seventy percent had fewer than 10 years of seniority in their current job ( M = 8.64, SD = 9.65). Only three people were retired (0.8%).

Morin’s 30-item meaning of work questionnaire (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ) along with sociodemographic questions (i.e., sex, age, job activities, and seniority at work) were conducted in French through an online platform. Answers to the meaning of work questionnaire were given on a 5-point Likert scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ).

Participants were recruited through various professional online social networks. This method does not provide for a true random sample but, owing to it resulting in a potentially larger range of respondents, it enlarges the heterogeneousness of the participants, even if it cannot ensure representativeness (Barberá & Zeitzoff, 2018 ; Hoblingre Klein, 2018 ). This point seems important because very homogenous samples were used in previous studies, especially with regard to professions.

Participants were volunteers, and were given the option of being able to stop the survey at any time. They received no compensation and no individual feedback. Participants were informed of these conditions before filling out the questionnaire. Oral and informed consent was obtained from all participants. Moreover, the Luxembourg Agency for Research Integrity (LARI on which the researchers in this study depend) specified that according to Code de la santé publique—Article L1123-7, it appears that France does not require research ethics committee [Les Comités de Protection des Personnes (CPP)] approval if the research is non-biomedical, non-interventional, observational, and does not collect personal health information, and thus CNR approval was not required.

Participants had to answer each question in order to submit the questionnaire: If one item was not answered, the respondent was not allowed to proceed to the next question. Thus, the database has no missing data. An introduction presented the subject of the study and its goals and guaranteed the participant’s anonymity. Researchers’ e-mail addresses were given, and participants were informed that they could contact the researchers for more information.

Data analyses

Three sets of statistical analyses were run on the data:

Analysis of the items, using traditional true score theory and item response theory, for verifying the psychometric qualities (using mainly R package “psych”). The main objectives of this part of analysis were to better understand the variability of respondents’ answers, to compute the discriminatory power of items, and to verify the distribution of items by using every classical descriptive indicator (mean, standard-deviation, skewness, and kurtosis), corrected item-total correlations, and functions of responses for distributions.

An exploratory factor analysis (EFA) with an oblimin rotation in order to define the latent structure of the meaning of work questionnaire, performed with the R packages “psych” and “GPArotation”. The structure we retained was based on adequation fits of various solutions (TLI, RMSEA and SRMR, see “List of abbreviations” section at the end of the article), and the use of R package “EFAtools” which helps to determine the adequate number of factors to retain for the EFA solution. Finally, this part of the analysis was concluded using calculations of internal consistency for each factor found in the scale.

A confirmatory factor analysis using the R package Lavaan and based on the results of the EFA, in order to verify that the latent structure revealed in Step c was valid and relevant for this meaning of work scale. The adequation between data and latent structure was appreciated on the basis of CFI, TLI, RMSEA, and SRMR (see “Abbreviations” section).

For step a, the responses of the complete sample were considered. For steps b and c, 183 subjects were selected randomly for each analysis from the total study sample. Thus, two subsamples comprised of completely different participants were used, one for the EFA in step b and one for the CFA in step c.

Because of the ordinal measurement of the responses and its small number of categories (5-point Likert), none of the items can be normally distributed. This point was verified in step a of the analyses. Thus, the data did not meet the necessary assumptions for applying factor analyses with conventional estimators such as maximum likelihood (Li, 2015 ; Lubke & Muthén, 2004 ). Therefore, because the variables were measured on ordinal scales, it was most appropriate to apply the EFA and CFA analyses to the polychoric correlation matrix (Carroll, 1961 ). Then, to reduce the effects of the specific item distributions of the variables used in the factor analyses, a minimum residuals extraction (MINRES; Harman, 1960 ; Jöreskog, 2003 ) was used for the EFA, and a weighted least squares estimator with degrees of freedom adjusted for means and variances (WLSMV) was used for the CFA as recommended psychometric studies (Li, 2015 ; Muthén, 1984 ; Muthén & Kaplan, 1985 ; Muthén & Muthén, 2010 ; Yang, Nay, & Hoyle, 2010 ; Yu, 2002 ).

The size of samples for the different analyses has been taken into consideration. A model structure analysis with 30 observed variables needs a recommended minimum sample of 100 participants for 6 latent variables, and 200 for 5 latent variables (Soper, 2019 ). The samples used in the present research corresponded to these a priori calculations.

Finally, according to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), acceptable and excellent model fits are indicated by CFI and TLI values greater than .90 and .95, respectively, by RMSEA values smaller than .08 (acceptable) and .06 (excellent), respectively, and SRMR values smaller than .08.

Item analyses

The main finding was the limited amount of variability in the answers to each item. Indeed, as Table 4 shows, respondents usually and mainly chose the answers agree and strongly agree , as indicated by the column of cumulated percentages of these response modalities (%). Thus, for all items, the average answer was higher than 4, except for item 11, the median was 4, and skewness and kurtosis indicators confirmed a systematic skewed on the left leptokurtic distribution. This lack of variability in the participants’ responses and the high average scores indicate nearly unanimous agreement with the propositions made about the meaning of work in the questionnaire.

Table 4 also shows that the items had good discriminatory power, expressed by corrected item-total correlations (calculated with all items) which were above .40 for all items. Finally, item analyses were concluded through the application of item response theory (Excel tools using the eirt add in; Valois, Houssemand, Germain, & Belkacem, 2011 ) which confirmed, by analyses of item characteristic curves (taking into account that item response theory models are parametric and assume that the item responses distributions follow a logistic function, Rasch, 1980 ; Streiner, Norman, & Cairney, 2015 , p. 297), the psychometric quality of each item and their link to an identical latent dimension. These different results confirmed the interest in keeping all items of the questionnaire in order to measure the work-meaning construct.

Exploratory analyses of the scale

A five-factor solution was identified. This solution explained 58% of the total variance in the responses of the scale items; the TLI was .885, the RMSEA was .074, and the SRMR was .04. The structure revealed by this analysis was relatively simple (saturation of one main factor for each item; Thurstone, 1947 ), and the communality of each item was high, except for item 11. The solution we retained presented the best adequation fits and the most conceptual explanation concerning the latent factors. Additionally, the “EFAtools” R package confirmed the appropriateness of the chosen solution. Table 5 shows the EFA results, which described a five-factor structure.

Nevertheless, the correlation matrix for the latent factors obtained by the EFA (see Table 6 ) suggested the existence of a general second-order meaning of work factor, because the five factors were significantly correlated each with others. This result could be described as the existence of a general meaning of work factor, which alone would explain 44% of the total variance in the responses.

Internal consistency of latent factors of the scale

The internal consistency of each latent factor, estimated by Cronbach alpha and McDonald omega, was high (above .80) and very high for the entire scale (α = .96 and ω = .97). Thus, for S uccess and Recognition at work and from work ’ s factor ω was .93, for Usefulness ’s factor ω was .92, for Respect ’s factor ω was .91, for Value from and through work ’s factor ω was slightly lower and equal to .85, and finally for Remuneration ’ s factor for which ω was .87.

Confirmatory factor analyses of the scale

In order to improve the questionnaire, we applied a CFA to this five-factor model to improve the model fit and refine the latent dimensions of the questionnaire. We used CFA to (a) determine the relevance of this latent five-factor structure and (b) confirm the relevance of a general second-order meaning-of-work factor. Although this procedure might appear redundant at first glance, it enabled us to select a definitive latent structure in which each item represents only one latent factor (simple structure; Thurstone, 1947 ), whereas the EFA that was computed in the previous step showed that certain items loaded on several factors. The CFA also easily verified the existence of a second-order latent meaning of work factor (the first-order loadings were .894, .920, .873, .892, and .918, respectively). Thus, this CFA was computed to complement the previous analyses by refining the latent model proposed for the questionnaire.

According to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), although the RMSEA value for the five-factor model was somewhat too high, the CFI and TLI values were excellent (χ 2 = 864.72, df = 400, RMSEA = .080, CFI = .989, TLI = .988). Table 7 presents the adequation fits for both solutions: a model with 5 first-order factors (as EFA suggests), and a model with 5 first-order factors and 1 second-order factor.

Figure 1 shows the model after the confirmatory test. This analysis confirmed the existence of a simple structure with five factors for the meaning of work scale and with a general, second-order factor of the meaning of work as suggested by the previous EFA.

Standardized solution of the structural model of the Meaning of Work Scale

The objective of this study was to verify the theoretical and psychometric structure of the meaning of work scale developed by Morin in recent years (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ). This scale has the advantages of being rather short, of proposing a multidimensional structure for the meaning of work, and of making it possible to assess the coherence between the aspects of work that are personally valued and the actual characteristics of the work environment. Thus, it can be used diagnostically or to guide individuals. To establish the structure of this scale, we analyzed deeply the items, and we implemented exploratory and confirmatory factor analyses, which we believe the scale’s authors had not carried out sufficiently. Moreover, we used a broad range of psychometric evaluation methods (traditional true score theory, item response theory, EFA, and structural equation modeling) to test the validity of the scale.

Item analyses confirmed results found in previous studies in which the meaning-of-work scale was administered. The majority of respondents agreed with the proposals of the questionnaire. Thus, this lack of variability is not specific to the present research and its sample (e.g., Morin & Cherré, 2004 ). Nevertheless, this finding can be explained by different reasons (which could be studied by other research) such as social desirability and the importance of work norms in industrial societies, or a lack of control regarding response bias.

The various versions of the latent structure of the scale proposed by the authors were not confirmed by the statistical analyses seen here. It nevertheless appears that this tool for assessing the meaning of work can describe and measure five different dimensions, all attached to a general factor. The first factor (F1), composed of nine items, is a dimension of recognition and success (e.g., item 17: work where your skills are recognized ; item 19: work where your results are recognized ; item 24: work that enables you to achieve the goals that you set for yourself ). It should thus be named Success and Recognition at work and from work and is comparable to dimensions from previous studies (personal success, Morin et al., 2001 ; social influence, Morin & Cherré, 2004 ). The second factor (F2), composed of seven items, is a dimension that represents the usefulness of work for an individual, whether that usefulness is social (e.g., Item 22: work that gives you the opportunity to serve others ) or personal (e.g., Item 28: work that enables you to be fulfilled ). It can be interpreted in terms of the Usefulness of work and generally corresponds to dimensions of the same name in earlier models (Morin, 2003 ; Morin & Cherré, 2004 ), although the definition used here is more precise. The third factor (F3), described by four items, refers to the Respect dimension of work (e.g., Item 5: work that respects human values ) and corresponds in part to the factors highlighted in prior studies (respect and rationalization of work, Morin, 2003 ; Morin & Cherré, 2004 ). The fourth factor (F4), composed of four items, refers to the personal development dimension and Value from and through work (e.g., Item 2: work that enables you to learn or to improve ). It is in some ways similar to autonomy and effectiveness, described by the authors of the scale (Morin, 2003 ; Morin & Cherré, 2004 ). Finally, the fifth and final factor (F5), with six items, highlights the financial and, more important, personal benefits sought or received from work. This includes physical and material safety and the enjoyment of work (e.g., item 14: work you enjoy doing ). This dimension of Remuneration partially converges with the aspects of personal values related to work described in previous research (Morin et al., 2001 ). Although the structure of the scale highlighted here differed from previous studies, some theoretical elements were nevertheless consistent with each other. To be convinced of this, the Table 8 highlights possible overlaps.

A second important result of this study is the highlighting of a second-order factor by the statistical analyses carried out. This latent second-level factor refers to the existence of a general meaning of work dimension. This unitary conception of the meaning of work, subdivided into different linked facets, is not in contradiction with the different theories related to this construct. Thus, Ros et al. ( 1999 ) defined the meaning of work as a personal interpretation of experiences and interaction at work. This view of meaning of work can confer it a unitary functionality for maintaining psychological harmony, despite the destabilizing events that are often a feature of work. It must be considered as a permanent process of work adjustment or work adaptation. In order to be effective, this adjustment needs to remain consistent and to be globally oriented toward the cognitive balance between the reality of work and the meaning attributed to it. Thus, it has to keep a certain coherence which would explain the unitary conception of the meaning of work.

In addition to the purely statistical results of this study, whereas some partial overlap was found between the structural model in this study and structural models from previous work, this paper provides a much-needed updating and improvement of these dimensions, as we examined several theoretical meaning of work models in order to explain them psychologically. Indeed, the dimensions defined here as Success and Recognition , Usefulness , Respect , Value , and Remuneration from the meaning of work scale by Morin et al. ( 2001 ) have some strong similarities to other theoretical models on the meaning of work, even though the authors of the scale referred to these models only briefly. For example, the dimensions work centrality as a life role , societal norms regarding work , valued work outcomes , importance of work goals , and work-role identification (MOW International Research team, 1987 ) concur with the model described in the present study. In the same manner, the model by Rosso et al. ( 2010 ) has some similarities to the present structure, and there is a conceptual correspondence between the five dimensions found here and those from their study ( individuation , contribution , self-connection , and unification ). Finally, Baumeister’s ( 1991 ), Morin and Cherré’s ( 2004 ), and Sommer, Baumeister, and Stillman ( 2012 ) studies presented similar findings on the meaning of important life experiences for individuals; they described four essential needs that make such experiences coherent and reasonable ( purpose , efficacy - control , rectitude , and self - worth ). It is obvious that the parallels noted here were fostered by the conceptual breadth of the dimensions as defined in these models. In future research, much more precise definitions are needed. To do so, it will be essential to continue running analyses to test for construct validity by establishing convergent validity between the dimensions of the various existing meaning of work scales.

It is also interesting to note the proximity between the dimensions described here and those examined in studies on the dimensions that characterize the work context (Pignault & Houssemand, 2016 ) or in Karasek’s ( 1979 ) and Siegrist’s ( 1996 ) well-known models, for example, which determined the impact of work on health, stress, and well-being. These studies were able to clearly show how dimensions related to autonomy, support, remuneration, and esteem either contribute to health or harm it. These dimensions, which give meaning to work in a manner that is similar to the dimensions highlighted in the current study (Recognition, Value, and Remuneration in particular), are also involved in health. Thus, it would be interesting to verify the relations between these dimensions and measures of work health.

Thus, the conceptual dimensions of the meaning of work, as defined by Morin ( 2003 ) and Morin and Cherré ( 1999 ), remained of strong theoretical importance even if, at the empirical level, the scale created on this basis did not correspond exactly. The present study has had the modest merit of showing this interest, and also of proposing a new structure of the facets of this general dimension. One of the major interests of this research can be found in the possible better interpretations that this scale will enable to make. As mentioned above, the Morin’s scale is very frequently used in practice (e.g., in state employment agencies or by Human Resources departments), and the divergent models of previous studies could lead to individual assessments of the meaning of work diverging, depending on the reading grid chosen. Showing that a certain similarity in the structures of the meaning of work exists, and that a general factor of the meaning of work could be considered, the results of the current research can contribute to more precise use of this tool.

At this stage and in conclusion, it may be interesting to consider the reasons for the variations between the structures of the scale highlighted by the different studies. There were obviously the different changes applied to the different versions of the scale, but beyond that, three types of explanation could emerge. At the level of methods, the statistics used by the studies varied greatly, and could explain the variations observed. At the level of the respondents, work remains one of the most important elements of life in our societies. A certain temptation to overvalue its importance and purposes could be at the origin of the broad acceptance of all the proposals of the questionnaire, and the strong interactions between the sub-dimensions. Finally, at the theoretical level, if, as our study showed, a general dimension of meaning of work seems to exist, all the items, all the facets and all the first order factors of the scale, are strongly interrelated at each respective level. As well, small variations in the distribution of responses could lead to variations of the structure.

The principal contribution of this study is undoubtedly the use of confirmatory methods to test the descriptive models that were based on Morin’s scale (Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ). The principal results confirm that the great amount of interest in this scale is not without merit and suggest its validity for use in research, both by practitioners (e.g., career counselors and Human Resources departments) and diagnostically. The results show a tool that assesses a general dimension and five subdimensions of the meaning of work with a 30-item questionnaire that has strong psychometric qualities. Conceptual differences from previous exploratory studies were brought to light, even though there were also certain similarities. Thus, the objectives of this study were met.

Limitations

As with any research, this study also has a certain number of limitations. The first is the sample size used for statistical analyses. Even if the research design respected the general criteria for these kind of analyses (Soper, 2019 ), it will be necessary to repeat the study with larger samples. The second is the cultural and social character of the meaning of work, which was not addressed in this study because the sample was comprised of people working in France. They can thus be compared with those in Morin’s studies ( 2003 ) because of the linguistic proximity (French) of the samples, but differences in the structure of the scale could be due to cultural differences between America and Europe. Nevertheless, other different international populations should be questioned about their conception of the meaning of work in order to measure the impact of cultural and social aspects (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ). In the same vein, a third limitation involves the homogeneity of the respondents’ answers. Indeed, there was quasi-unanimous agreement with all of the items describing work (see Table 4 and previous results, Morin & Cherré, 2004 ). It is worth examining whether this lack of variance results from a work norm that is central and promoted in industrialized countries as it might mask broader interindividual differences. Thus, this study’s protocol should be repeated with other samples from different cultures. Finally, a fourth limitation that was mentioned previously involves the validity of the scale. Concerning the content validity and because some items loaded similarly different factors, it could be interesting to verify the wording content of the items, and potentially modify or replace some of them. The purpose of the present study was not to change the content of the scale but to suggest how future studies could analyze this point. Concerning the construct validity, this first phase of validation needs to be followed by other phases that involve tests of convergent validity between the existing meaning of work scales as well as tests of discriminant validity in order to confirm the existence of the meaning of work construct examined here. In such studies, the centrality of work (Warr, 2008 ; Warr, Cook, & Wall, 1979 ) should be used to confirm the validity of the meaning of work scale. Other differential, individual, and psychological variables related to work (e.g., performance, motivation, well-being) should also be introduced in order to expand the understanding of whether relations exist between the set of psychological concepts involved in work and individuals’ jobs.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author.

Abbreviations

Confirmatory factor analyses

Comparative Fit Index

Exploratory factor analyses

Luxembourg Agency for Research Integrity

• Meaning of work

Tucker Lewis Index of factoring reliability

Root mean square error of approximation

Standardized root mean square residual

Aronsson, G., Bejerot, E., & Häremstam, A. (1999). Healthy work: Ideal and reality among public and private employed academics in Sweden. Personal Public Management , 28 (2), 197–215. https://doi.org/10.1177/009102609902800203 .

Article   Google Scholar  

Barberá, P., & Zeitzoff, T. (2018). The new public address system: why do world leaders adopt social media? International Studies Quarterly , 62 (1), 121–130. https://doi.org/10.1093/isq/sqx047 .

Baumeister, R. F. (1991). Meaning of life . New York: Guilford.

Google Scholar  

Bendassolli, P. F., & Borges-Andrade, J. E. (2013). Meaningfulness in work in Brazilian and French creative industries. Spanish Journal of Psychology , 16 , 1–15. https://doi.org/10.1017/sjp.2013.107 .

Bendassolli, P. F., Borges-Andrade, J. E., Coelho Alves, J. S., & de Lucena Torres, T. (2015). Meaningful work scale in creative industries: A confirmatory factor analysis. Psico-USF , 20 (1), 1–12. https://doi.org/10.1590/1413-82712015200101 .

Brief, A. P. (1991). MOW revisited: A brief commentary. European Work and Organizational Psychology , 1 , 176–182. https://doi.org/10.1080/09602009108408523 .

Brief, A. P., & Nord, W. R. (1990). Meaning of occupational work . Toronto: Lexington Books.

Carroll, J. B. (1961). The nature of the data, or how to choose a correlation coefficient. Psychometrika , 26 , 247–272. https://doi.org/10.1007/bf02289768 .

Dawis, R. V., Lofquist, L. H., & Weiss, D. J. (1968). A theory of work adjustment (a revision). Minnesota Studies in Vocational Rehabilitation, XXIII , 47 , 1–14. https://doi.org/10.1016/b978-0-08-013391-1.50030-4 .

England, G. W. (1991). The meaning of working in USA: Recent changes. The European Work and Organizational Psychologist , 1 , 111–124.   https://doi.org/10.1111/j.1464-0597.1990.tb01036.x  

England, G. W., & Harpaz, I. (1990). How working is defined: National contexts and demographic and organizational role influences. Journal of Organizational Behavior , 11 , 253–266. https://doi.org/10.1002/job.4030110402 .

Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance , 16 , 250–279. https://doi.org/10.1016/0030-5073(76)90016-7 .

Harman, H. H. (1960). Modern Factor Analysis . Chicago: The University of Chicago Press.

Harpaz, I. (1986). The factorial structure of the meaning of work. Human Relations , 39 , 595–614. https://doi.org/10.1177/001872678603900701 .

Harpaz, I., & Fu, X. (2002). The structure of the meaning of work: A relative stability amidst change. Human Relations , 55 , 639–−668. https://doi.org/10.1177/0018726702556002 .

Harpaz, I., & Meshoulam, I. (2010). The meaning of work, employment relations, and strategic human resources management in Israel. Human Resource Management Review , 20 , 212–223. https://doi.org/10.1016/j.hrmr.2009.08.009 .

Heider, F. (1946). Attitudes and cognitive organization. Journal of Psychology , 21 , 107–112. https://doi.org/10.1080/00223980.1946.9917275 .

Hoblingre Klein, H. (2018). Réseaux sociaux professionnels: instruments d’empowerment professionnel ?: Analyse de cas de consultants RH et de recruteurs sur LinkedIn. PhD manuscript, Education, Université de Strasbourg. Retrieved from https://tel.archives-ouvertes.fr/tel-02133972 .

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling , 6 , 1–55. https://doi.org/10.1080/10705519909540118 .

Jöreskog, K. G. (2003). Factor analysis by MINRES. Retrieved from http://www.ssicentral.com/lisrel/techdocs/minres.pdf

Karasek, R. A. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly , 24 , 285–308. https://doi.org/10.2307/2392498 .

Ketchum, L. D., & Trist, E. (1992). All teams are not created equal. How employee empowerment really works . Newbury Park: Sage.

Kline, R. B. (2011). Principles and practices of structural equation modeling , (3rd ed., ). New-York: Guilford.

Li, C. H. (2015). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood diagonally weighted least squares. Behavior Research Method , 1–14. https://doi.org/10.3758/s13428-015-0619-7 .

Lips-Wiersma, M., & Wright, S. (2012). Measuring the meaning of meaningful work: Development and validation of the Comprehensive Meaningful Work Scale (CMWS). Group & Organization Management , 37 (5), 655–685. https://doi.org/10.1177/1059601112461578 .

Lubke, G. H., & Muthén, B. O. (2004). Applying multigroup confirmatory factor models for continuous outcomes to Likert scale data complicates meaningful group comparisons. Structural Equation Modeling , 11 , 514–534. https://doi.org/10.1207/s15328007sem1104_2 .

McKee-Ryan, F., Song, Z., Wanberg, C. R., & Kinicki, A. J. (2005). Psychological and physical well-being during unemployment: A meta- analytic study. Journal of Applied Psychology , 90 , 53–76. https://doi.org/10.1037/0021-9010.90.1.53 .

Morin, E. (2003). Sens du travail. Définition, mesure et validation. In C. Vandenberghe, N. Delobbe, & G. Karnas (Eds.), Dimensions individuelles et sociales de l’investissement professionnel , (pp. 11–20). Louvain: Presses Universitaires de Louvain.

Morin, E. (2006). Donner un sens au travail. In Document—Centre de recherche et d’intervention pour le travail, l’efficacité organisationnelle et la santé (CRITEOS), HEC Montréal .

Morin, E. (2008). Sens du travail, santé mentale et engagement organisationnel . Montréal: IRSST.

Morin, E., Archambault, M., & Giroux, H. (2001). Projet Qualité de Vie au Travail. Rapport Final . Montréal: HEC Montréal.

Morin, E., & Cherré, B. (1999). Les cadres face au sens du travail. Revue Française de Gestion , 126 , 83–93. https://doi.org/10.3166/rfg.251.149-164 .

Morin, E., & Cherré, B. (2004). Réorganiser le travail et lui donner du sens. In A. Lancry, & C. Lemoine (Eds.), La personne et ses rapports au travail , (pp. 87–102). Paris: L’Harmattan.

Morse, N. C., & Weiss, R. S. (1955). The function and meaning of work and the job. American Sociological Review , 20 , 191–198. https://doi.org/10.2307/2088325 .

MOW International Research team (1987). The meaning of working . London: Academic Press.

Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika , 49 , 115–132. https://doi.org/10.1007/bf02294210 .

Muthén, L. K., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology , 38 , 171–189. https://doi.org/10.1111/j.2044-8317.1985.tb00832.x .

Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide , (6th ed., ). Los Angeles: Muthén & Muthén.

Osborne, J. W. & Fitzpatrick, D. C. (2012). Replication analysis in exploratory factor analysis: what it is and why it makes your analysis better. Practical Assessment, Research & Evaluation , 17 (15), 1–8. https://doi.org/10.7275/h0bd-4d11 .

Osgood, C. E., & Tannenbaum, P. H. (1955). The principle of congruity in the perception of attitude change. Psychological Review , 62 , 42–55. https://doi.org/10.1037/h0048153 .

Article   PubMed   Google Scholar  

Pignault, A., & Houssemand, C. (2016). Construction and initial validation of the Work Context Inventory. Journal of Vocational Behavior , 92 , 1–11. https://doi.org/10.1016/j.jvb.2015.11.006 .

Pratt, M. G., & Ashforth, B. E. (2003). Fostering meaningfulness in working and at work. In K. S. Cameron, J. E. Dutton, & R. E. Quinn (Eds.), Positive organizational scholarship , (pp. 309–327). San Francisco: Berrett-Koehler Publishers.

Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests . Chicago: The University of Chicago Press.

Roe, R. A., & Ester, P. (1999). Values and Work: Empirical findings and theoretical perspective. Applied Psychology: An international review , 48 ( 1 ), 1–21. https://doi.org/10.1111/j.1464-0597.1999.tb00046.x .

Ros, M., Schwartz, S. H., & Surkiss, S. (1999). Basic individual values, work values, and meaning of work. Applied Psychology: An international review , 48 (1), 49–71. https://doi.org/10.1111/j.1464-0597.1999.tb00048.x .

Rosso, B. D., Dekas, K. H., & Wrzesniewski, A. (2010). On the meaning of work: A theoretical integration and review. Research in Organizational Behavior , 30 , 91–127. https://doi.org/10.1016/j.riob.2010.09.001 .

Ruiz-Quintanilla, S. A. & England, G. W. (1994). How working is defined: Structure and stability. Working paper, Cornell University.

Siegrist, J. A. (1996). Adverse health effects of high-effort/low-reward conditions. Journal of Occupational Health Psychology , 1 , 27–41. https://doi.org/10.1037/1076-8998.1.1.27 .

Sommer, K. L., Baumeister, R. F., & Stillman, T. F. (2012). The construction of meaning from life events: Empirical studies of personal narratives. In P. T. P. Wong (Ed.), The Human Quest for Meaning , (pp. 297–314). New York: Routledge.

Soper, D.S. (2019). A-priori sample size calculator for structural equation models [Software]. Available from http://www.danielsoper.com/statcalc .

Steers, R. M., & Porter, L. (1979). Work and motivation: An evaluative summary. In R. M. Steers, & L. Porter (Eds.), Motivation and work behaviour , (pp. 555–564). New-York: McGraw-Hill.

Steger, M. F., Dik, B. J., & Duffy, R. D. (2012). Measuring meaningful work: The work and meaning inventory (WAMI). Journal of Career Assessment , 20 (3), 322–337. https://doi.org/10.1177/1069072711436160 .

Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health measurement scales. Oxford Medicine Online. https://doi.org/10.1093/med/9780199685219.001.0001 .

Super, D. E., & Sverko, B. (1995). Life roles, values, and careers . San Francisco: Jossey-Bass Publishers.

Sverko, B. (1989). Origin of individual differences in importance attached to work: A model and a contribution to its evaluation. Journal of Vocational Behavior , 34 , 28–39. https://doi.org/10.1016/0001-8791(89)90062-6 .

Sverko, B., & Vizek-Vidovic, V. (1995). Studies of the meaning of work: Approaches, models, models and some of the findings. In D. E. Super, & B. Sverko (Eds.), Life roles, values, and Careers , (pp. 3–21). San Francisco: Jossey-Bass Publishers.

Thurstone, L. L. (1947). Multiple-factor analysis . Chicago: University of Chicago Press.

Topalova, V. (1994). Changes in the attitude to work and unemployment during the period of social transition. In R. A. Roe, & V. Russinova (Eds.), Psychosocial aspects of employment: European perspectives , (pp. 21–28). Tilburg: Tilburg University Press.

Valois, P., Houssemand, C., Germain, S., & Belkacem, A. (2011). An open source tool to verify the psychometric properties of an evaluation instrument. Procedia Social and Behavioral Sciences , 15 , 552–556. https://doi.org/10.1016/j.sbspro.2011.03.140 .

Wanberg, C.R. (2012) The Individual Experience of Unemployment. Annual Review of Psychology, 63 (1), 369-396. https://doi.org/10.1146/annurev-psych-120710-100500

Warr, P. (2008). Work values: Some demographic and cultural correlates. Journal of Occupational and Organizational Psychology , 81 , 751–775. https://doi.org/10.1348/096317907x263638 .

Warr, P. B., Cook, J. D., & Wall, T. D. (1979). Scales of measurement of some work attitudes and aspects of psychological well-being. Journal of Occupational Psychology , 52 , 129–148. https://doi.org/10.1111/j.2044-8325.1979.tb00448.x .

Wrzesniewski, A., Dutton, J. E., & Debebe, G. (2003). Interpersonal sensemaking and the meaning of work. Research in Organizational Behavior , 25 , 93–135. https://doi.org/10.1016/s0191-3085(03)25003-6 .

Wrzesniewski, A., McCauley, C., Rozin, P., & Schwartz, B. (1997). Jobs, careers, and callings: people’s relations to their work. Journal of Research in Personality , 31 , 21–33. https://doi.org/10.1006/jrpe.1997.2162 .

Yang, C., Nay, S., & Hoyle, R. H. (2010). Three approaches to using lengthy ordinal scales in structural equation models. Parceling, latent scoring, and shortening scales. Applied Psychological Measurement , 34 , 122–142. https://doi.org/10.1177/0146621609338592 .

Article   PubMed   PubMed Central   Google Scholar  

Yu, C.-Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Dissertation . Los Angeles: University of California.

Zanders, H. (1993). Changing work values. In P. Ester, L. Halman, & R. de Moor (Eds.), The individualizing society. Value changes in Europe and North-America , (pp. 129–153). Tilburg: Tilburg University Press.

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Pignault, A., Houssemand, C. What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire. Psicol. Refl. Crít. 34 , 2 (2021). https://doi.org/10.1186/s41155-020-00167-4

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What Makes Work Meaningful?

• Evgenia I. Lysova,
• Luke Fletcher,
• Sabrine El Baroudi

Research shows that being more aware of yourself and your surroundings is key.

How do you make your work more meaningful? Prior studies have focused on understanding the factors that contribute to making work meaningful overall, such as having more autonomy or being able to job craft. But these are individual actions that don’t easily translate into how we experience meaningfulness every day. It can also be difficult for early career professionals as you can’t just decide to drop every uninspiring task from your to-do list in an attempt to experience more meaning in your role.

• Research shows that being in a state of awareness can help. In a state of awareness (of yourself and your wider work environment), people are more willing and able to be creative in how they think and deal with challenges and other work-related problems. Awareness also helps you come up with better solutions, interpret signals from others around you, and adapt to changing circumstances. This, in turn, can facilitate a sense of meaning because it enables you to think and behave in ways that help you see the value, worth, and impact within everyday work tasks and interactions.
• To become more aware, start by practicing mindfulness. Mindfulness helps us learn to recognize and acknowledge what’s going on in the mind, moment by moment, increases awareness, and decreases rumination. It also promotes cognitive flexibility, all of which lead to greater meaning-making.
• Journaling is a great way to build awareness into your everyday work life. Before you end the day, ask yourself, “What did I find meaningful today,” and write it down. You can do this not only for yourself but also for your colleagues. Consider weaving awareness into group discussions and conversations at work.
• Investing more in one’s relationships is important to feel happy and fulfilled at work, as our findings suggest. As an individual, you can respectfully engage with others at work through active listening and showing appreciation. These behaviors could then also enable greater psychological safety in the work environment as they help promote a sense of belonging at work that prior research shows is critical for meaningfulness

We all search for meaning in our lives, and many of us find it through our work . In fact, research shows that meaningfulness is more important to us than any other aspect of our jobs — including pay and rewards, opportunities for promotion, and working conditions. When we experience our work as meaningful, we’re more engaged, committed, and satisfied. When we don’t, we’re more willing to quit , and this is especially true for younger workers .

• Evgenia I. Lysova an Associate Professor in Organizational Behavior at the Management and Organization department of Vrije Universiteit Amsterdam, the Netherlands. Her main research interests concern the topic of meaningful work, work as a calling, careers, and Corporate Social Responsibility. She is on a mission to enable and sustain greater experiences of meaningfulness in individuals’ work and careers with the help of organizations.
• LF Luke Fletcher is an Associate Professor in Human Resource Management at the University of Bath’s School of Management, UK. His research interests span both organizational psychology and strategic human resource management, and include topics such as meaningful work, employee engagement, diversity and inclusion, and LGBT+ workers.
• SB Sabrine El Baroudi is an Assistant Professor in Organizational Behavior and Human Resource Management at the Department of Management and Organization, Vrije Universiteit Amsterdam, the Netherlands. She is also a director of the VU Knowledge Hub for Feedback Culture. Her main research interests are proactive work and career behaviors, feedback, meaningful work, and other (green) HRM-related topics. She is particularly interested in examining how these topics influence performance and work behaviors at different organizational levels; that is, individual, team, and organizational levels.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Supplementary information:, table of contents, i. background, ii. themes from consultation and research, a. workforce system alignment, iii. regulations, a. definition of exit, b. work outcomes data sources, c. federal matching for calculating work outcomes of tanf exiters, d. state-level matching for the supplemental work outcomes report, e. secondary school diploma or its recognized equivalent attainment rate, iv. justification for interim final rule, v. collection of information requirements, b. request for feedback, c. review and approval of the information collection, vi. regulatory review and analysis, list of subjects in 45 cfr part 265, part 265—data collection and reporting requirements, enhanced content - submit public comment.

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Office of Family Assistance (OFA), Administration for Children and Families (ACF), Department of Health and Human Services (HHS).

Interim final rule.

This interim final rule modifies ACF regulations in order to implement the statutory changes enacted by section 304 of the Fiscal Responsibility Act of 2023 (FRA) related to the reporting of work outcomes under the Temporary Assistance for Needy Families (TANF) program. ACF is promulgating this rule as an interim final rule to ensure states and territories have sufficient time to comply with data collection for fiscal year 2025.

This interim final rule (IFR) is effective on October 1, 2024. Comments on this IFR must be received on or before December 26, 2024.

ACF encourages the public to submit comments electronically to ensure they are received in a timely manner. You may submit comments, identified by Regulatory Information Number (RIN) 0970-AD04, by any of the following methods:

• Federal eRulemaking Portal: https://www.regulations.gov . Follow the instructions for submitting comments.

Instructions: All submissions received must include the agency name and RIN (0970-AD04) for this rulemaking. All comments received will be posted without change to https://www.regulations.gov,including any personal information provided.

We will not consider comments received beyond the 180-day comment period in modifying the interim final rule. You may find the following suggestions helpful for preparing your comments:

• Be specific;
• Address only issues raised by the rulemaking in the interim final rule and the information collections, not the changes to the statute itself;
• Explain reasons for any objections or recommended changes;
• Propose appropriate alternatives; and
• Reference the specific section of the interim final rule being addressed.

You can obtain copies of the proposed collection of information and submit comments by emailing [email protected] . Identify all requests by the title of the information collection.

Lauren Frohlich, TANF Data Division, Office of Family Assistance, ACF, at [email protected] or 202-401-9275. Deaf and hard of hearing individuals may call 202-401-9275 through their chosen relay service or 711 between 8 a.m. and 7 p.m. Eastern Time.

E. Secondary School Diploma or its Recognized Equivalent Attainment Rate

The Fiscal Responsibility Act (FRA) of 2023, Public Law 118-5 , requires each state, in consultation with the Secretary of the Department of Health and Human Services (HHS), to collect and report information relating to work outcomes measures for work-eligible individuals in the Temporary Assistance for Needy Families (TANF) Program. Section 304 of the legislation requires HHS to issue regulations implementing these new requirements. It states, “in order to ensure nationwide comparability of data, the Secretary, after consultation with the Secretary of Labor and with States, shall issue regulations governing the reporting of performance indicators under this subsection.”

We are updating the existing TANF data regulations ( 45 CFR part 265 , Data Collection and Reporting Requirements) to reflect the new reporting requirements. “Each state . . . shall collect and submit to the Secretary the information necessary for each indicator. . . .” Section 304. “State” is defined to mean “the 50 States of the United States, the District of Columbia, the Commonwealth of Puerto Rico, the United States Virgin Islands, Guam, and American Samoa.” 42 U.S.C. 619 (5). States and territories must begin reporting on those requirements in fiscal year (FY) 2025. For the remainder of the preamble, we will use the term “states” to refer to states and territories. These provisions do not apply to Tribal TANF programs.

Section 304 of the FRA specifies that to ensure nationwide comparability of data, all states must collect and submit “the information necessary” to determine four indicators of performance. These are:

• Employment Rate—2nd Quarter After Exit: The percentage of individuals who were work-eligible individuals as of the time of exit from the program, who are in unsubsidized employment during the second quarter after the exit;
• Employment Retention Rate—4th Quarter After Exit: The percentage of individuals who were work-eligible individuals as of the time of exit from the program who were in unsubsidized employment in the second quarter after the exit, who are also in unsubsidized Start Printed Page 53871 employment during the fourth quarter after the exit;
• Median Earnings—2nd Quarter After Exit: The median earnings of individuals who were work-eligible individuals as of the time of exit from the program, who are in unsubsidized employment during the second quarter after the exit; and
• Secondary School Diploma or its Recognized Equivalent Attainment Rate: The percentage of individuals who have not attained 24 years of age, are attending high school or enrolled in an equivalency program, and are work-eligible individuals or were work-eligible individuals as of the time of exit from the program, who obtain a high school degree or its recognized equivalent while receiving assistance under the state program funded under this part or within one year after the exit.

Further, the statute required HHS to consult with states and the Department of Labor (DOL) before issuing regulations. In response, HHS engaged in consultation including issuing a Request for Information (RFI) ( 88 FR 82902 , November 27, 2023) providing states and the general public with an opportunity to provide input, hosted listening sessions with state TANF leadership and data leads, held meetings with the DOL and the Department of Education (ED), and had discussions with practitioners and researchers as part of the Workforce IT Support Center Steering Committee and the National Association of Welfare Research Statistics annual conference. Through these discussions, we identified promising practices and recommendations for policy options.

Written responses to ACF's RFI included responses from state TANF agencies and social services departments, national associations that represent state and county human services agencies, and a small number of advocacy groups. The use of “state” in the summary of comments below refers to both states who responded directly and associations who collected comments and responded on behalf of member states.

TANF's new statutory work outcomes measures are similar to the performance measures established by the Workforce Innovation and Opportunity Act of 2014 (WIOA). DOL and ED shared lessons learned from implementing WIOA that inform our implementation of TANF's work outcomes measures, such as factors impacting the implementation timeline and data quality across sources. States also shared their experiences with WIOA and several respondents encouraged ACF—in partnership with DOL—to support opportunities for state workforce and human services agencies to engage in peer learning and information sharing around the new measures. They requested ACF, where possible, foster the alignment through shared definitions, data sources, and report timeframes. They noted that supporting alignment with WIOA outcome reporting strengthens collaboration between TANF and the broader workforce system, which will yield operational efficiencies across programs, identification of promising practices, a more client-centered culture, and improved service delivery.

We appreciate the time and attention that respondents gave to reviewing the RFI and preparing their comments. This IFR seeks to make the new reporting requirements as useful as possible for program improvement, considers the challenges of implementation, and provides flexibility for states when possible.

The Office of Family Assistance (OFA) is committed to improving the equitable administration of all OFA programs for the children and families we serve. In the RFI, we asked in what ways equity should be considered when implementing work outcome measures.

Respondents expressed gratitude for our consideration of equity in the implementation of the work outcomes measures. All noted the advantages of collecting and reporting data disaggregated by race, ethnicity, age, disability, and other demographic characteristics or geography.

We appreciate the importance of understanding outcome disparities in the TANF program, and we are committed to providing increased technical assistance to states on how to use their own data to understand outcomes disparities in the TANF program so that they can better ensure equity throughout the system.

Respondents also made suggestions for practices in the context of work outcomes that could support equity. We remain committed to supporting states as they identify and explore meaningful ways of addressing disparities and ensuring equity.

The following discussion provides information on the changes we are making in 45 CFR part 265 . We discuss how we will operationalize the statutory changes enacted in the FRA related to the reporting of work outcomes under the TANF program.

In § 265.2, “What definitions apply to this part?”, we added definitions of “exit” and “unsubsidized employment” for the purpose of calculating the Work Outcomes of TANF Exiters Report and the Secondary School Diploma or its Recognized Equivalent Attainment Rate. We also defined “secondary school diploma” and its “recognized equivalent.”

We added two new reporting requirements. States are required to submit the data required for the Work Outcomes of TANF Exiters Report quarterly and the Secondary School Diploma or its Recognized Equivalent Attainment Rate annually. The regulation also includes the option for states to voluntarily submit their own calculated work outcomes measures. The regulation details what to include in the supplemental report for states who choose to submit one. In addition to submitting the Work Outcomes of TANF Exiters Report, any state that does not have an Unemployment Insurance program and thus is currently unable to submit quarterly wage data to the ACF-designated wage match source will be required to submit the Supplemental Work Outcomes Report.

Other revisions:

• Section 265.1 What does this part cover?—replaced outdated reference to section 413 (rankings of State TANF programs) with section 411(f).
• Section 265.3 What reports must the State file on a quarterly basis?— added quarterly report Work Outcomes of TANF Exiters Report and details of (1) Employment Rate—2nd Quarter After Exit; (2) Employment Retention Rate—4th Quarter After Exit; and (3) Median Earnings—2nd Quarter After Exit.
• Section 265.4 When are quarterly reports due?—added Work Outcomes of TANF Exiters Report.
• Section 265.5 May States use sampling?—clarified that sampling is not allowed for either Work Outcomes of TANF Exiters Report or Secondary School Diploma or its Recognized Equivalent Attainment Rate and moves that clause to paragraph (d).
• Section 265.6 Must States file reports electronically?—added Work Outcomes of TANF Exiters Report, Secondary School Diploma or its Recognized Equivalent Attainment Rate, and Supplemental Work Outcomes Report.
• Section 265.7 How will we determine if the State is meeting the quarterly reporting requirements?— Start Printed Page 53872 added requirements for Work Outcomes of TANF Exiters Report.
• Section 265.9 What information must the State file annually?—added paragraphs (f), the Secondary School Diploma or its Recognized Equivalent Attainment Rate, and (g), Supplemental Work Outcomes Report.
• Section 265.10 When are annual reports due?—clarified due dates of all annual reports in § 265.9.

The work outcomes measures depend on a definition of what it means to “exit” the TANF program. The legislation defines “exit” as, “with respect to a State program funded under this part, ceases to receive assistance under the program funded by this part.”

We believe, and states concurred during consultation, that further clarification of what it means to “exit” the TANF program is necessary to ensure consistency across states' interpretations of the exiting population. Research studies and state respondents warned about the issue of “churn,” in which individuals and families cycle on and off the TANF caseload due to temporary jobs, shifting life circumstances, sanctions, or terminations of assistance for not meeting program administrative requirements. We note that TANF “leavers” studies from the early 2000s often defined a “leaver” as someone who has left cash assistance for at least two months, while WIOA defines a “common exit” as a participant not receiving DOL-administered services for at least 90 days. [ 1 ] DOL, states, researchers, and advocacy organizations all recommended that the TANF work outcomes measures align with WIOA's performance measures as much as possible, in order to support coordination between TANF and WIOA programs and their shared participants. That approach is supported by the statutory language requiring the Department to consult with DOL. For these reasons, and to account for the impact of churn, we are defining “exit” as a family having not received TANF assistance for at least 90 days. We are using “family” in the definition to account for the cases where, for example, due to sanction, the state removes the needs of the work-eligible individual from the assistance payment while continuing to provide assistance to the family. In other words, a work-eligible individual will be included as an exiter in these measures only when their family ceases to receive assistance, and will not meet the definition of an exiter when the needs of the work-eligible individual are removed from the assistance payment but the family continues to receive assistance. An individual in the family must have been “a work-eligible individual,” as defined in 45 CFR 261.2(n)(1) , in their last month of assistance.

Multiple respondents to the RFI stressed the importance of taking into account the reasons someone would exit TANF. Some individuals may have been taken off TANF because of a sanction for noncompliance with work activities, while others obtained jobs or moved out of state. States are currently required to report to ACF on the reasons for case closure in section two (2) of the TANF Data Report (ACF-199). However, states interpret and use the reporting categories of reasons for case closure inconsistently. For instance, on average, states reported that 34 percent of closed cases were closed due to “other/unknown” reasons, but across states that percentage ranged from 5 to 98 percent in FY 2022. [ 2 ] While OFA has decided not to use this IFR to modify the data reporting elements for case closure, we encourage states to improve the quality of their reporting for the existing data element in the TANF Data Report (ACF-199). OFA will include recommendations in guidance to states on how to improve the quality of this data element.

We note that some states may move individuals out of the TANF program and into separate state programs (funded by maintenance-of-effort funds) or solely state-funded programs (not reported as maintenance-of-effort). For some states, these may be distinctly different programs, while for other states the difference may just be the funding source. The statute clearly states “with respect to State program funded under this part” which refers only to the TANF program, and not separate state programs or solely state-funded programs. Therefore, when considering who exited TANF for this data collection, states should include those work-eligible individuals who were moved to separate state programs or solely-state funded programs and have not received TANF-funded assistance in at least 90 days.

States are proud of the work they are doing to improve TANF outcomes. Some states are already collecting and studying data for employment, retention, and income and are eager to share the results and best practices. These states explained both the benefits and drawbacks of the systems they use. Many included specific timelines, charts, and other visuals to depict the level of complexity involved. Others offered their templates and experience to ACF as we develop guidance and technical assistance products.

States that have already implemented similar measures rely on wage data for administration of state programs, using a combination of sources including state departments of labor, state unemployment insurance wage records, the State Wage Interchange System (SWIS), and the National Directory of New Hires (NDNH). [ 3 ]

Others expressed trepidation around the new measures. Most notably, they expressed concern around the resources and time required to implement data sharing agreements, system changes, and new processes. These commenters welcome a national approach that allows them to maintain their focus on program improvement.

Nearly all respondents noted the value in standard measures and the utility of having a centralized data match at the Federal level to meet the requirement of “nationwide comparability of data” in section 304 of the FRA. They also noted that the employment landscape can vary from region to region and state to state. They requested that ACF provide appropriate context when measures are published. Respondents noted macroeconomics (cost of living, unemployment rates, geography, state minimum wage, impacts of natural disasters etc.), program design and policy (county-administered versus state, benefit caps, political climate), and participant demographics (sex, gender, race, income, family status, disability, formerly incarcerated) as some of the factors that inform the outcome measures.

Taking into consideration the comments we received, ACF is Start Printed Page 53873 instituting a two-pronged approach: Federal matching for calculating Work Outcomes of TANF Exiters and a Supplemental Work Outcomes Report. Consultations with states and DOL, ED, and others informed our two-pronged approach and raised several important considerations. States expressed concerns about the implementation timeline if state-level data sharing agreements were needed, but other states were concerned about the lag in results with a Federal match that would make the outcomes less useful. ACF heard from various stakeholders that, while a Federal-level match with the NDNH has the benefit of reporting wages earned across state lines and from Federal employment, state-level matches could include other sources of wage data such as self-employment or gig work. Each data source and methodology has its own strengths and shortcomings, and these new measures provide an opportunity for learning about the best tools for assessing and improving outcomes for TANF exiters. More information about Federal matching for calculating Work Outcomes of TANF Exiters and the Supplemental Work Outcomes Report is below.

For the first three statutory measures ( i.e., work outcomes of TANF exiters), states will report information to ACF that is necessary to calculate the measures of work outcomes of TANF exiters at the Federal level. Specifically, states will be required to submit Social Security Numbers (SSNs) of all work-eligible individuals who exit TANF in a given quarter on a quarterly basis. ACF will then match those SSNs with quarterly wage records in the NDNH, which is a national database of wage and employment information on most American workers administered by ACF's Office of Child Support Services. [ 4 ] In FY 2022, over 752 million quarterly wage records were submitted to the NDNH. [ 5 ] ACF will use these matched results to compute the first three work outcomes measures on behalf of states. This approach will allow for standardized measures and will not require states to initiate new data-sharing agreements at the state level. We understand that Guam does not currently have an Unemployment Insurance program and therefore does not submit quarterly wage data to the NDNH. Guam will still need to submit the Work Outcomes of TANF Exiters report so that ACF is able to capture outcomes of individuals who find work outside of Guam after exiting the Guam TANF Program. In addition, any state, like Guam, that does not have an Unemployment Insurance program and thus is currently unable to submit quarterly wage records to the ACF-designated wage match source will be required to submit the Supplemental Work Outcomes Report. That report will be used to calculate the same performance measures as those in the Work Outcomes of TANF Exiters Report. We will work closely with Guam to assess the data and provide technical assistance to support calculating the performance measures.

The Work Outcomes of TANF Exiters measures have various operational timelines. The measures themselves specify employment and earnings at different intervals following exit. Further, as noted above, the definition of “exit” includes a 90 day wait period. ACF will provide subsequent guidance on and technical support regarding which “exiters” to include in each quarterly data submission. ACF will run matches each quarter so that states will not need to track or re-identify work-eligible individuals the second and fourth quarters after they exited TANF.

ACF will continue to explore how to share information gained from the Federal-level match to make the data available to and useful for state TANF programs, in addition to satisfying the reporting requirements of the FRA. This may involve providing preliminary match results to states on a quarterly basis before the data have settled and been finalized.

Several states requested the option to provide additional data that would enrich the outcomes generated from a centralized data match. The states that have systems and data-sharing agreements in place believe those systems to be timelier and more comprehensive. Notably, some states reported that they will continue to produce their own performance measure reports due not only to local statutory requirements but also to support program partnership and continuous improvement. ACF wants to empower states to analyze their own data for program improvement and policy decision-making. To support this effort, these regulations establish an additional Supplemental Work Outcomes Report to be submitted annually by interested states. This will allow states that already have performance reporting infrastructure in place to provide calculated outcomes measures results with alternative data sources. The report will include documentation of data sources and methodology to assess validity and support ongoing learning and identification of best practices. ACF will encourage this voluntary submission as a way for states and the Federal government to promote and learn more about alternative data sources as compared to matching to wage data at the Federal level. ACF will report on findings from the Supplemental Work Outcomes Report as part of ongoing learning and context for state performance measures. This report will be mandatory for any state, like Guam, that does not have an Unemployment Insurance program and is thus unable to submit quarterly wage data to the ACF-designated wage match source. The Supplemental Work Outcomes Report also provides an avenue for continued alignment with WIOA performance measures; states that have highly coordinated TANF and workforce agencies could demonstrate the benefits of state-level data matching (potentially through SWIS) and the addition of supplemental wage information, such as for those who are self-employed or participate in gig work and are not systematically captured in quarterly wage records. ACF is committed to providing technical assistance and support to states interested in developing their own infrastructure to calculate work outcomes, including helping develop relationships across state agencies, data system modifications, data sharing agreements, and data analysis capacity. ACF began this work with the TANF Data Collaborative  [ 6 ] and plans to continue to find innovative ways to support states as they focus on better outcomes for TANF recipients and exiters.

ACF has experience with TANF outcomes measures similar to the ones that the Work Outcomes for TANF Exiters Report will capture, through the Start Printed Page 53874 former High Performance Bonus measures, 42 U.S.C. 603(a)(4) , and more recently for performance measures that are reported as part of the Congressional Budget Justification. However, ACF has little experience collecting information related to the Secondary School Diploma or its Recognized Equivalent Attainment Rate (hereafter Secondary School Diploma Attainment Rate) for TANF participants and exiters. States reported to us that they also have little experience with this type of measure and anticipated that it would be more difficult to implement.

The Secondary School Diploma Attainment Rate measure presents its own unique challenges, and states have requested support for implementing this measure. Participant surveys may be the most direct way to obtain the data; however, states requested technical assistance to increase survey response rates. In addition, states want guidance for navigating the complex network of educational systems and setting up data-sharing agreements with the various entities involved.

We learned from our research, consultations, and the RFI that some states have existing longitudinal databases and cross-department agreements that would be well-suited for calculating these measures. Other states did not have this type of existing access and infrastructure. The ED, DOL, and respondents to the RFI emphasized that there are multiple ways a person may earn a secondary school diploma or recognized equivalent, including but not limited to adult school/education, community college, or public or private high school within K-12 systems. Local education agencies often have different geographical maps from TANF offices or workforce investment boards. For some states, secondary school equivalency testing may be independent of state government, while for others, such testing may be managed by state education agencies.

The wide range of structures and readiness for collecting information for this measure across states led us to the decision to leave the data source selection up to the states, following sub-regulatory guidance from ACF. ACF will provide thorough guidance and technical support for the calculation of the rate, including who belongs in the numerator and denominator. States must use universe-level data in their calculations, meaning that the rate should be based on the entire population that meets the criteria and not a sample of that population.

ACF recognizes that the typical secondary academic calendar is at odds with the Federal fiscal year and annual submission of the data for the Secondary School Diploma Attainment Rate. This is further complicated by the range of timelines of the various data sources and tracking individuals after exit. We note that the Secondary School Diploma Attainment Rate submission is on a longer lag because of the nature and complexity of the data and the 1-year post-exit wait period. ACF will provide guidance on and technical support regarding the reporting periods that should be covered in a given annual report. We acknowledge states are only able to submit information known to them at the time of reporting and commit to providing additional guidance to manage the level of effort associated with tracking this small subset of TANF participants.

ACF intends to support states in finding innovative ways to collect this information. ACF will ask for sources and methodology as part of the reporting to assess validity of the measure and to support ongoing learning and identification of best practices. Our Federal partners have learned a lot already from states' implementation of WIOA's credential attainment measure, which has some helpful parallels.

The Administrative Procedure Act (APA) generally requires agencies to follow certain procedures when promulgating rules. 5 U.S.C. 551 et seq. Under section 553(b)(B)) of the APA, however, a notice of proposed rulemaking is not required when an agency, for good cause, finds (and incorporates the finding and a brief statement of reasons in the rule issued) that notice and public comment is impracticable, unnecessary, or contrary to the public interest. We find that good cause exists because, under the specific circumstances here, it is impracticable to provide an opportunity for notice and comment prior to issuing this rule.

Providing notice and comment before issuing this rule was impracticable because of the limited time period following the statute's enactment in June 2023 for ACF to consult with states and DOL and then promulgate regulations with sufficient lead-time for states to make necessary system changes to comply with the statutory reporting obligations beginning on October 1, 2024. States will need to plan for, budget, and implement systems changes to comply with data collection and reporting requirements for fiscal year 2025. This may require states to reallocate their budgets, modify existing contracts, and/or enter into new ones, modify and/or enter into new data sharing agreements, and create and/or modify systems for data collection. We are issuing an IFR so that states have time to complete each of these necessary steps before the October 1, 2024, compliance date.

It also was not practicable to provide notice and comment because we needed time to consult with the DOL and with states, as required by section 304 of the FRA, prior to issuing regulations. In addition to being statutorily mandated, the consultation period was necessary to allow ACF to obtain state input on a number of technical questions before moving forward with the rule making process. This technical information from states was key to the agency's thinking and approach around this rulemaking. Consultations with DOL and ED began in July 2023, just one month after the statute was enacted, and have continued throughout the development of this regulation.

The consultation period, described in greater detail in section II above, included a RFI which was published on November 27, 2023. ACF had received 24 comments on the RFI by January 11, 2024. The RFI outlined several possible approaches and factors that needed to be considered and sought public comment. Those comments informed the approach set forth in this IFR.

In addition to issuing the RFI, ACF met with states and conducted listening sessions between September 2023 and January 2024. Almost all states, whether during listening sessions or in response to the RFI, requested that ACF expedite the issuance of regulations and develop guidance as soon as possible. Many respondents wrote that it will take several months or more to set up the necessary infrastructure required to respond to the requirements of the FRA. States specifically requested that the Department convey as soon as possible how it is interpreting Congress' definition of the statutory term “exit” so that states could provide the specifications necessary to their contractors or in-house developers so that they could begin the task of system changes. In anticipation of the implementation, some states have initiated system changes without the definition of “exit,” whereas others are waiting on guidance so that they do not risk costly updates that need to be modified. Further delay of the regulation could lead to states applying the statute ineffectively or inconsistently, which could ultimately reduce the data quality and comparability of the measures. Our goal has been to issue regulations as soon as possible so that states can make sound decisions on the allocation of resources Start Printed Page 53875 and operationalize plans in time for the fiscal year 2025 reporting deadlines.

We believe that issuing an IFR is in the interest of the entities that must meet the reporting requirements, and the effective date for this IFR is justified and reasonable. Although this IFR is being published with an effective date of October 1, 2024, we encourage interested parties to provide comments through December 26, 2024, so that we may have the benefit of public participation in advance of issuing a final rule. ACF will modify the IFR's provisions if warranted by public comments. As we implement the IFR, we welcome public comments on any relevant implementation issues, and we will take those comments into consideration in developing the final rule.

The Paperwork Reduction Act (PRA), 44 U.S.C. 3501 et seq., provides that a Federal agency generally cannot conduct or sponsor a collection of information, and the public is generally not required to respond to an information collection, unless it is approved by the Office of Management and Budget (OMB) under the PRA and displays a currently valid OMB Control Number. In addition, notwithstanding any other provisions of law, no person shall generally be subject to penalty for failing to comply with a collection of information that does not display a valid Control Number. See 5 CFR 1320.5(a) and 1320.6 .

This interim final rule includes the following new information collections.

• Work Outcomes of TANF Exiters Report
• Secondary School Diploma or its Recognized Equivalent Rate
• Supplemental Work Outcomes Report

As required by PRA, we will submit the proposed information collection(s) to OMB for review and approval.

Work Outcomes of TANF Exiters Report: Quarterly, states will be required to submit Social Security numbers (SSNs) of all work-eligible individuals who exit TANF in a given quarter. Each state must file the report within 45 days following the end of the quarter (QE). The report must contain the SSNs of all individuals who are confirmed to have exited during the reporting period. An individual's exit date is confirmed when 90 days have elapsed since the participant last received assistance. States submit SSNs 45 days after the QE in which their exit date was confirmed. For example:

An individual exits on November 23, 2024 (FY 2025, Quarter [Q]1). For the state to confirm the exit date, 90-days must elapse since the participant last received assistance. The exit date is confirmed on February 21, 2025 (FY 2025, Q2). The individual's SSN is included as an exiter in the quarter ending (QE) March 31, 2025 report (FY 2025, Q2), covering reporting period October 1, 2024-December 31, 2024 (FY 2025, Q1).

ACF will then match the SSN with quarterly wage records in the NDNH to obtain records from two quarters after the individual's exit (FY 2025, Q3) through four quarters after the individual's exit (FY 2026, Q1). ACF will use the matched results to compute the measures on behalf of states: Employment Rate—2nd Quarter After Exit; Employment Retention Rate—4th Quarter After Exit; and Median Earnings—2nd Quarter After Exit.

Secondary School Diploma Attainment Rate: Annually, states will be asked to submit their calculated rate following the definitions and formula set by ACF. The report must include documentation of methodology and data sources.

Supplemental Work Outcomes Report: Annually, states have the option to submit the state's calculation of the first three work outcomes, following the definitions and formulas set by ACF. The report must include documentation of methodology and data sources. Any state like Guam that does not have an Unemployment Insurance program and thus is currently unable to submit quarterly wage records to the ACF-designated wage match source will be required to submit the Supplemental Work Outcomes Report.

In compliance with the requirements of Section 3506(c)(2)(A) of the PRA, the ACF is soliciting public comment on the specific aspects of the information collection described above. You can request copies of the proposed collections by emailing [email protected] . ACF requests comments on these new collections, including but not limited to the quality, utility, and clarity of the information to be collected and the estimated time to complete.

ACF is particularly interested in feedback on the estimated time to complete each of the new information collections. Currently, ACF is estimating the time to complete as follows:

In estimating time to complete, you should include the time associated with activities necessary to complete the requests. This should include the time associated with the following example activities (as applicable):

• Reviewing instructions
• Compiling information or materials to respond
• Acquiring, installing, and utilizing technology and systems
• Updating current technology and systems
• Completing and reviewing collected information
• Finalizing and sending information

Submit comments to [email protected] by August 27, 2024. Consideration will be given to comments and suggestions submitted within the timeframe specified.

The comments received in response to this notification will fulfill the requirement for a 60-day comment period in compliance with the requirements of section 3506(c)(2)(A) of the PRA. We will submit an information collection request for these new proposed collections to OMB for review and approval following consideration of public comment. These requirements will not become effective until approved by OMB.

We have examined the impacts of the final rule under Executive Order 12866 , Start Printed Page 53876 Executive Order 13563 , Executive Order 14094 , the Regulatory Flexibility Act ( 5 U.S.C. 601-612 ), the Congressional Review Act/Small Business Regulatory Enforcement Fairness Act ( 5 U.S.C. 801 , Pub. L. 104-121 ), and the Unfunded Mandates Reform Act of 1995 ( Pub. L. 104-4 ).

Executive Orders 12866, 13563, and 14094 direct us to assess all benefits, costs, and transfers of available regulatory alternatives and, when regulation is necessary, to select regulatory approaches that maximize net benefits (including potential economic, environmental, public health and safety, and other advantages; distributive impacts; and equity). Rules are “significant” under Executive Order 12866 , section 3(f)(1) (as amended by Executive Order 14094 ), if they “have an annual effect on the economy of $200 million or more (adjusted every 3 years by the Administrator of [the Office of Information and Regulatory Affairs (OIRA)] for changes in gross domestic product); or adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, territorial, or tribal governments or communities.” This interim final rule implements the data collection and reporting requirements of section 304 of the Fiscal Responsibility Act of 2023, which could entail a small incremental increase in time spent by state governments for these activities. Thus, this interim final rule is not a significant regulatory action under Executive Order 12866 , section 3(f)(1).

Because this rule is not likely to result in an annual effect on the economy of $100 million or more or meet other criteria specified in the Congressional Review Act/Small Business Regulatory Enforcement Fairness Act, this interim final rule does not fall within the scope of 5 U.S.C. 804(2) .

The Regulatory Flexibility Act requires us to analyze regulatory options that would minimize any significant impact of a rule on small entities. The Secretary certifies that this interim final rule will not result in a significant impact on a substantial number of small entities. The primary impact is on state governments, which are not considered small entities under the RFA.

The Unfunded Mandates Reform Act of 1995 (UMRA) generally requires that each agency conduct a cost-benefit analysis, identify and consider a reasonable number of regulatory alternatives, and select the least costly, most cost-effective, or least burdensome alternative that achieves the objectives of the rule before promulgating any proposed or final rule that includes a Federal mandate that may result in expenditures of more than $100 million (adjusted for inflation) in at least one year by state, local, and tribal governments, or by the private sector. The current threshold after adjustment for inflation using the Implicit Price Deflator for the Gross Domestic Product is $183 million, reported in 2023 dollars. This interim final rule will not result in an unfunded mandate in any year that meets or exceeds this amount.

• Grant programs—social programs
• Public assistance programs
• Reporting and recordkeeping requirements

For the reasons discussed in the preamble, the OFA amends 45 CFR part 265 as follows:

1. The authority citation for part 265 is revised to read as follows:

Authority: 42 U.S.C. 603 , 605 , 607 , 609 , 611 , and 613 .

2. Amend § 265.1 by:

a. Revising paragraph (a);

b. Removing footnote 1 from paragraph (b) introductory text;

c. Revising paragraphs (b)(2) and (3); and

d. Adding paragraphs (b)(4) and (5).

The revisions and additions read as follows:

(a) This part explains how we will collect the information required by section 411(a) of the Act (data collection and reporting); the information required to implement section 407 of the Act (work participation requirements), as authorized by section 411(a)(1)(A)(xii); the information required to implement section 409 (penalties), section 403 (grants to States), section 405 (administrative provisions), section 411(b) (report to Congress), and section 411(f) (reporting performance indicators); and the data necessary to carry out our financial management and oversight responsibilities.

(2) The expenditure data in the quarterly TANF Financial Report (or, as applicable, the Territorial Financial Report);

(3) The definitions and other information on the State's TANF and MOE programs that must be filed annually;

(4) The definitions and other information necessary for the Work Outcomes of TANF Exiters Report; and

(5) The definitions and other information necessary for the Secondary School Diploma or its Recognized Equivalent Attainment Rate.

3. Amend § 265.2 by:

a. Revising paragraph (a); and

b. Adding paragraphs (c) through (e).

The revision and additions read as follows:

(a) Except as provided in paragraphs (b) through (e) of this section, the general TANF definitions at §§ 260.30 through 260.33 and the definitions of a work-eligible individual and the work activities in § 261.2 of this chapter apply to this part.

(c) For purposes of the Work Outcomes of TANF Exiters Report and the Secondary School Diploma or its Recognized Equivalent Attainment Rate, exit is the date that a family with a work-eligible individual ceases to receive assistance (as defined in § 260.31) from the TANF program. The last day of assistance cannot be determined until 90 days have elapsed since the participant last received assistance.

(d) For purposes of the Work Outcomes of TANF Exiters Report, unsubsidized employment means full- or part-time employment in the private or public sector after exiting the TANF program.

(e) For purposes of the Secondary School Diploma or its Recognized Equivalent Attainment Rate:

(1) A secondary school diploma is a “regular high school diploma” as that term is defined in 21 U.S.C. 7801(43) , the Elementary and Secondary Education Act of 1965 (ESEA), as amended by the Every Student Succeeds Act (ESSA).

(2) A recognized equivalent to a secondary school diploma is a certification recognized by a State that signifies that a student has completed the State's requirements for a high school education.

4. Amend § 265.3 by:

a. Revising paragraph (a)(1); and

b. Adding paragraph (g).

The revision and addition read as follows:

(1) Each State must collect on a monthly basis, and file on a quarterly basis, the data specified in the TANF Data Report, the TANF Financial Report (or, as applicable, the Territorial Financial Report), and the Work Outcomes of TANF Exiters Report.

(g) Work Outcomes of TANF Exiters Report. Each State must file the Social Security numbers of all work-eligible individuals, as defined in § 261.2(n), who have exited the program, as defined in § 265.2(b). This information will be used for calculating the following Work Outcomes performance indicators:

(1) Employment Rate—2nd Quarter After Exit: the percentage of individuals who were work-eligible individuals as of the time of exit from the program, who are employed during the second quarter after the exit;

(2) Employment Retention Rate—4th Quarter After Exit: the percentage of individuals who were work-eligible individuals as of the time of exit from the program who were employed in the second quarter after the exit, who are also employed during the fourth quarter after the exit; and

(3) Median Earnings—2nd Quarter After Exit: the median earnings of individuals who were work-eligible individuals as of the time of exit from the program, who are employed during the second quarter after the exit.

5. Amend § 265.4 by adding paragraph (d) to read as follows:

(d) Each State must file the Work Outcomes of TANF Exiters Report within 45 days following the end of the quarter.

6. Amend § 265.5 by:

a. Removing the last sentence of paragraph (a); and

b. Adding paragraph (d).

The addition reads as follows:

(d) States may not use sampling to report expenditure data, data included in the Work Outcomes of TANF Exiters Report, or data included in the Secondary School Diploma or its Recognized Equivalent Attainment Rate.

7. Revise § 265.6 to read as follows:

Each State must file all reports ( i.e., the TANF Data Report, the TANF Financial Report (or, as applicable, the Territorial Financial Report), the SSP-MOE Data Report, the Work Outcomes of TANF Exiters Report, and the Secondary School Diploma or its Recognized Equivalent Attainment Rate) electronically, based on format specifications that we will provide.

8. Amend § 265.7 by:

b. Redesignating paragraphs (e) and (f) as paragraphs (f) and (g); and

c. Adding new paragraph (e).

(a) Each State's quarterly reports (the TANF Data Report, the TANF Financial Report (or Territorial Financial Report), the SSP-MOE Data Report, and the Work Outcomes of TANF Exiters Report) must be complete and accurate and filed by the due date.

(e) For the Work Outcomes of TANF Exiters Report, “complete and accurate report” means that:

(1) The reported data accurately reflect information available to the State in case records, and automated data systems;

(2) The State reports data on all applicable elements ( i.e., no data are missing); and

(3) The State reports universe data on all work eligible individuals who exited TANF in a particular quarter.

9. Amend § 265.9 by:

a. Removing footnote 7 from paragraph (c)(9); and

b. Adding paragraphs (f) and (g).

The additions read as follows:

(f) Each State must submit the percentage of individuals who have not attained 24 years of age, are attending high school or enrolled in an equivalency program, and are work-eligible individuals or were work-eligible individuals as of the time of exit from the program, who obtain a high school degree or its recognized equivalent while receiving assistance under the State program funded under this part or within one year after the individuals exit from the program. The Secondary School Diploma or its Recognized Equivalent Attainment Rate report must include methodology and documentation of data sources.

(g) On a voluntary basis, a State may also submit calculated work outcomes measures that follow the definitions of the Work Outcomes of TANF Exiters (as defined in § 265.3(g)) based on alternative data sources. The report must include documentation of data sources. In addition to the Work Outcomes of TANF Exiters Report, this Supplemental Work Outcomes Report is mandatory for any State that is unable to submit quarterly wage data to the ACF-designated wage match source.

10. Revise § 265.10 to read as follows:

The annual reports required by § 265.9 are due 45 days after the end of the fiscal year.

Xavier Becerra,

Secretary, Department of Health and Human Services.

1.  See https://aspe.hhs.gov/​tanf-leavers-applicants-caseload-studies and https://www.dol.gov/​agencies/​eta/​performance/​definitions .

2.   Characteristics and Financial Circumstances of TANF Recipients, Fiscal Year 2022, Office of Family Assistance, Administration for Children and Families ( https://www.acf.hhs.gov/​ofa/​data/​characteristics-and-financial-circumstances-tanf-recipients-fiscal-year-2022 ).

3.  State Welfare (“TANF” or “IV-A”) Agencies are authorized to request NDNH information to carry out state responsibilities under programs funded under part A of title IV of the Social Security Act, specifically 42 U.S.C. 653(j)(3) . Section 304 of the FRA amended section 411 of the Social Security Act ( 42 U.S.C. 611 ) to specify that each state, in consultation with the Secretary of HHS, shall collect and submit the information necessary for the reporting of work outcomes for fiscal year 2025 and each fiscal year thereafter.

4.  For more detail on the NDNH: https://www.acf.hhs.gov/​sites/​default/​files/​documents/​ocse/​a_​guide_​to_​the_​national_​directory_​of_​new_​hires.pdf .

5.  FY 2022 Preliminary Data Report and Tables from Table P-97 ( https://www.acf.hhs.gov/​css/​policy-guidance/​fy-2022-preliminary-data-report-and-tables ).

6.  The TANF Data Collaborative was a 30-month pilot program that offered technical assistance and training to support cross-disciplinary teams of staff at eight state and county TANF programs in the routine use of TANF and other administrative data to inform policy and practice. Learn more about the program and participants here: https://www.acf.hhs.gov/​opre/​report/​tanf-data-collaborative-pilot-profiles-collection-data-analytics-projects-state-county .

[ FR Doc. 2024-13865 Filed 6-27-24; 8:45 am]

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What are penny stocks?

• Potential rewards
• How to find penny stocks 

Penny Stocks: High-Risk, High-Reward Investments

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• Penny stocks are securities that trade at less than $5 per share, often in unsupervised over-the-counter (OTC) markets.
• Penny stocks are considered lucrative but high-risk investments: volatile, illiquid, and often subject to scams.
• Investors interested in penny stocks should deal with those listed on larger exchanges and sold by established brokers.

Penny stocks have become more popular than ever, tempting investors with a low cost of entry and the prospect of significant financial gains. Stories of shares making gains of over 4,000% in just months add to their appeal, and new trading technology makes it easier than ever to enter the market.

But while they can be lucrative, penny stocks come with significant risk. Potential investors should be careful to understand what they're getting into.

Definition 

Penny stocks refer to company stocks that cost, if not merely a penny, a pretty low amount. FINRA describes them as "typically stocks issued by very small companies that trade at less than $5 per share." Because they're often sold "over the counter" (OTC), rather than through centralized stock exchanges, they are also sometimes called OTC stocks. 

Depending on the issuing company's market capitalization — the total dollar value of its outstanding shares — penny stocks can be referred to as small-cap, micro-cap, or nano-cap stocks.

Characteristics 

Penny stocks have a short list of common characteristics. Being familiar with these is crucial if you want to know how to invest in penny stocks. 

Here are some penny stock characteristics you should know: 

Low market capitalization - Companies whose shares are considered penny stocks frequently have a low market capitalization (market cap). 

Limited financial resources — Penny stocks often represent ownership rights in companies that have limited cash and financial resources. Since they are small and unproven, they may have a hard time getting access to credit that other larger, more established companies can use. 

Low trading volume — Many penny stocks have rather light trading volume. This can have several adverse consequences, including low liquidity and sharp volatility. 

The low liquidity may prevent investors from selling penny stocks at the prices they want, and this can have a significant impact on their returns. 

Low trading volume can also create a significant difference between bids (what buyers want to pay to buy) and asks (what sellers want to sell). 

High volatility — One of the major risks of penny stock investing is the volatility involved. There isn't a huge market for these particular securities, meaning there may not be many buyers and sellers, and this can result in penny stocks suffering sharp price movements. 

Securities with lower trading volume are also more susceptible to experiencing sharp fluctuations as a result of trades made by "whales" or large players. 

The risks of investing in penny stocks 

Volatility .

Volatile price fluctuations can help deliver huge gains to investors. But they can also deliver massive losses. Those 20% to 100% price moves aren't always in an upward direction. Many have warned that those who purchase penny stocks should be ready for the possibility that they could lose their entire investment. 

Penny stocks can be highly volatile because their markets are smaller than their larger counterparts, making them more susceptible to severe price fluctuations. 

Lack of liquidity 

Penny stocks can suffer from lackluster liquidity. Because the markets for these securities are thinly traded, investors may encounter difficulty buying or selling these stocks at their desired price. 

Further, they may face a hard time selling penny stocks when they want to. 

The low liquidity also makes these stocks vulnerable to sharp price fluctuations. 

Limited information

Investors may find it more difficult to conduct thorough due diligence on penny stocks than on shares of larger companies as a result of several different factors. 

Companies offering their shares in this manner don't have the same disclosure requirements as businesses selling their shares through more established marketplaces. 

Businesses whose shares trade on the large, centralized exchanges file their financial reports to the Securities and Exchange Commission (SEC), and the reports are available to investors for free. Until September 2020, these reports were not required of companies issuing penny stocks.

The SEC has recently issued new rules to increase information and improve investor protections. Brokers are now prohibited from quoting a price for a penny stock unless the issuing company has publicly released its current financials. 

Further, companies offering penny stocks may have limited historical data since they are frequently unproven businesses that have not spent much time in the market. 

Susceptibility to fraud 

The lack of information and transparency is one reason that fraud is so common in the penny stock market. 

One common scheme is called the "pump and dump." Scammers purchase huge quantities of a stock and then share misleading information to make it attractive to other investors. In some cases, individuals even create fake shell companies that do not actually do any business or have any assets. 

Believing that the stock is a good investment, investors buy shares, causing the price to rise. The scammers then sell off their shares, earning huge profits and causing the share price to collapse. Investors are then left holding worthless stocks. 

The potential rewards of penny stocks 

High growth potential .

Penny stocks can potentially provide some very compelling returns, if the companies they represent experience significant growth. Every big company had to start somewhere, and it is entirely possible that any little acorn can grow to be a big oak tree. 

It is also possible that while certain shares may start out as penny shares, they may be available through major exchanges, like the Nasdaq or NYSE, later on. 

Affordable entry point 

The low price of penny stocks may make them more accessible to investors with smaller budgets. You would have to spend thousands of dollars to get a lot of shares of Microsoft or Apple — if you were buying full shares and not fractional shares — but you can spend a lot less to get in on the penny stock market. The idea of buying shares of a solid startup at $0.20 and cashing out at $1 — or even much more — is tempting to many investors.

Another perfectly valid consideration is that penny stocks can give you the opportunity to get involved in a ground-floor opportunity. 

It's every investor's dream: Catch an unknown star before it gets discovered, and ride it when it starts to soar. Strange as it sounds, Amazon ( AMZN ) was one such business at one point. Back in 1997, you could buy Amazon shares for $1.68; as late as 1998, you could get them for $5. Amazon is currently trading for a lot more than that now .

How to find penny stocks 

Otc markets .

There are many specific OTC markets where investors can buy and sell penny stocks. One example of such a market is OTCQB , which is specifically tailored to startup companies and other businesses that are just getting started. 

To list shares on this exchange, shares must have a bid price of no less than $0.01. Also, the companies these penny stocks represent must meet certain regulatory and reporting requirements. 

Another marketplace where you can buy and sell penny stocks is the Pink Market , which enables a variety of companies, including those unwilling to disclose financial information, to list their shares. Investors should keep in mind that businesses offering penny stocks through the Pink Market, or Pink Sheets, do not need to meet the same stringent listing requirements as major exchanges. 

Brokerage platforms 

Some online brokers allow investors to purchase penny stocks. Investors should keep in mind that the brokerages that offer these low-price securities can run the gamut in terms of quality. 

Fortunately, there are some major financial institutions that let their customers buy and sell penny stocks. Investors should be sure to conduct thorough due diligence on any broker they are thinking about using. 

Stock screeners 

By using a stock screener, you can save yourself a lot of time and energy when searching for penny stocks worthy of your investment. Leveraging one of these tools, you can scan the markets for penny stocks that meet specific criteria. 

Keep in mind that not all stock screeners allow you to search for penny stocks, so make sure you use one that does. 

Tips for investing in penny stocks 

Do your research .

One of the best penny stock tips is to do your research. Evaluate any potential investment thoroughly. Don't trust unsolicited emails, chatrooms, or cold calls. Instead, contact your state securities regulator or the SEC to get accurate information about any company you are considering as a potential investment. 

Invest only what you can afford to lose 

Penny stocks are extremely volatile, meaning they can produce compelling returns or cause you to lose all your money. You may want to avoid choosing penny stocks unless you are willing to lose your entire investment. 

Diversify your portfolio 

One of the best ways to manage the downside risk (risk of losing your investment) that comes with penny stocks is creating a diversified portfolio . For example, you could combine your high-risk penny stocks with lower-risk shares of more-established companies that will probably experience lower volatility. 

Set a stop-loss order 

Another way you can manage risk (by managing potential losses) is setting up stop-loss orders. These are orders that automatically fulfill if the share in question reaches a certain price, for example if one of your penny stocks falls to a specific price. 

By setting one of these up, you can protect your investment by reducing your potential losses. 

SME definition

Small and medium-sized enterprises (SMEs) represent 99% of all businesses in the EU. The definition of an SME is important for access to finance and EU support programmes targeted specifically at these enterprises.

What is an SME?

Small and medium-sized enterprises (SMEs) are defined in the EU recommendation 2003/361 .

The main factors determining whether an enterprise is an SME are

• staff headcount
• either turnover or balance sheet total

These ceilings apply to the figures for individual firms only. A firm that is part of a larger group may need to include staff headcount/turnover/balance sheet data from that group too.

Further details include

• The revised user guide to the SME definition (2020) (2 MB, available in all EU languages)
• Declaring your enterprise to be an SME (the form is available in all languages as an annex in the revised user guide)
• The SME self-assessment tool which you can use to determine whether your organisation qualifies as a small and medium-sized enterprise

What help can SMEs get?

There are 2 broad types of potential benefit for an enterprise if it meets the criteria

• eligibility for support under many EU business-support programmes targeted specifically at SMEs: research funding, competitiveness and innovation funding and similar national support programmes that could otherwise be banned as unfair government support ('state aid' – see block exemption regulation )
• fewer requirements or reduced fees for EU administrative compliance

Monitoring of the implementation of the SME definition

The Commission monitors the implementation of the SME definition and reviews it in irregular intervals. Pursuant to the latest evaluation, the Commission concluded that there is no need for a revision.

On 25 October 2021, we informed stakeholders by holding a webinar with presentations on the SME evaluation's results and next steps.

Supporting documents

• Study to map, measure and portray the EU mid-cap landscape (2022)
• Staff working document on the evaluation of the SME definition  (2021)
• Executive summary on the evaluation of the SME definition  (2021)
• Q&A on the evaluation of the SME definition  (2021)
• Final report on evaluation of the SME definition  (2018) (10 MB)
• Final report on evaluation of the SME definition (2012)  (1.8 MB)
• Executive summary on evaluation of the SME definition (2012)  (345 kB)
• Implementing the SME definition (2009)  (50 kB)
• Implementing the SME definition (2006)  (40 kB)

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National and international civil society organisations (CSOs) are key partners in monitoring development co-operation policies and programmes. Development co-operation can also be channelled to or through CSOs: 

Aid is characterized as going to CSOs when it is in the form of core contributions and contributions to programmes, with the funds programmed by the CSOs. 

Aid is characterized as going through CSOs when funds are channeled through these organisations to implement donor-initiated projects. This is also known as earmarked funding.

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